Comparison of bulk sequencing, SMARTseq2 data and 10X single cell data
Load the packages used
library("tidyr")
library("stringr")
library("tibble")
library("pheatmap")
library("Seurat")Loading required package: ggplot2
Loading required package: cowplot
Attaching package: ‘cowplot’
The following object is masked from ‘package:ggplot2’:
ggsave
Loading required package: Matrix
Attaching package: ‘Matrix’
The following object is masked from ‘package:tidyr’:
expandlibrary("ggrepel")
library("dplyr")
Attaching package: ‘dplyr’
The following object is masked from ‘package:Biobase’:
combine
The following objects are masked from ‘package:BiocGenerics’:
combine, intersect, setdiff, union
The following objects are masked from ‘package:stats’:
filter, lag
The following objects are masked from ‘package:base’:
intersect, setdiff, setequal, unionlibrary("forcats")Load all the data
We have three different sequencing experiments and want to cross compare between
- Bulk sequencing of NSCs, Neuroblasts, Microglia and Endothelial Cells
- Single Cell RNA sequencing of Neural Stem cells with
- SMARTseq2 protocol: qNSC1 - aNSC2
- 10X protocol: qNSC1 - NB + OPC & OD
Define colors for young and old throughout the analysis
age_colors <- c(young = "yellowgreen" , old = "slateblue")
age_colors_bulk <- c(young = "yellowgreen" , old = "slateblue" , Mo7 = "slateblue1")Load the bulk sequencing TPM values
bulk_data <- read.csv(file = "../Bulk_sequencing/count_table/Gene_expression_matrix_population.csv" , row.names = 1)
bulk_data <- as.matrix(bulk_data)Log2 the TPM values
bulk_data <- log2(bulk_data+1)Also we load the annotation
bulk_annotation <- read.csv(file = "../Bulk_sequencing/annotation/annotation_bulk.csv" , row.names = 1)bulk_annotation$type <- factor(x = bulk_annotation$type , levels = c("NSC","Neuroblast","Microglia","Endothelial"))
bulk_annotation$age <- factor(x = bulk_annotation$age , levels = c("young","Mo7","old"))Which celltypes do we have?
bulk_celltypes <- unique(as.character(bulk_annotation$type))
bulk_celltypes[1] "NSC" "Neuroblast" "Endothelial" "Microglia" Load the SMARTseq2 sequencing TPM values
smartseq2_data <- read.csv(file = "../SmartSeq2/count_table/TPM_NSCs.csv") %>% dplyr::select(-X) %>% column_to_rownames(var = "ensembl_gene_id")
smartseq2_data <- as.matrix(smartseq2_data)Log2 the TPM values
smartseq2_data <- log2(smartseq2_data+1)Also we load the annotation
smartseq2_annotation <- read.csv(file = "../SmartSeq2/count_table/NSCs_annotation.csv" , row.names = 1)smartseq2_annotation$type <- fct_recode(.f = smartseq2_annotation$type , qNSC1 = "q1" , qNSC2 = "q2" , aNSC1 = "a1" , aNSC2 = "a2")
smartseq2_annotation$type <- factor(x = smartseq2_annotation$type , levels = c("qNSC1","qNSC2","aNSC1","aNSC2"))
smartseq2_annotation$age <- factor(x = smartseq2_annotation$age , levels = c("young","old"))Which celltypes do we have?
smartseq2_celltype_colors <- c( qNSC1 = "steelblue" , qNSC2 = "steelblue1" , aNSC1 = "tomato" , aNSC2 = "sienna1")Load the 10X sequencing normalized values
seurat_10X2 <- readRDS(file = "../10X/seurat_10X2_clustered_min_1500_nGene.RDS" )We can extract the normalized expression from the object
tenx_data <- expm1( seurat_10X2@data )
tenx_data <- as.matrix(tenx_data)Log2 the TPM values
tenx_data <- log2(tenx_data+1)Also we extract the annotation from the object
tenx_annotation <- FetchData(object = seurat_10X2 , vars.all = c("celltype","age") ) %>% rownames_to_column("cell") %>% dplyr::rename(type = celltype)tenx_annotation$type <- factor(x = tenx_annotation$type , levels = c("qNSC1","qNSC2","aNSC0","aNSC1","aNSC2","TAP","NB","OPC","OD"))
tenx_annotation$age <- factor(x = tenx_annotation$age , levels = c("young","old"))Which celltypes do we have?
tenx_celltype_colors <- c( qNSC1 = "steelblue" , qNSC2 = "steelblue1" , aNSC0 = "tomato" , aNSC1 = "sienna1", aNSC2 = "sienna3" , TAP = "green" , NB = "yellow" , OPC = "pink" , OD = "violet")Also we can load the genes.tsv file from the CellRanger Output which contains a mapping of ENSEMBL Gene IDs to the Gene names used in the 10X object
tenx_gene_2_ensembl_tbl <- read.table(file = "../10X/count_table/filtered_gene_bc_matrices_mex/mm10/genes.tsv") %>% dplyr::rename( ensembl_gene_id = V1 , gene_symbol = V2 )We can translate all the gene symbols to ENSEMBL Gene IDS
tenx_ensembl_gene_id <- unlist(lapply( X = rownames(tenx_data) , FUN = function(gene){
as.character( tenx_gene_2_ensembl_tbl[ which(tenx_gene_2_ensembl_tbl$gene_symbol == gene) , "ensembl_gene_id" ] )[1]
} ))Add the ENSEMBL Gene IDs as rownames
tenx_data_ensembl <- tenx_data
rownames(tenx_data_ensembl) <- tenx_ensembl_gene_idLoad differentially expressed gene lists
Bulk sequencing
NSCs
bulk_DE_genes_NSC <- read.csv(file = "../Bulk_sequencing/DESeq2_results_old_vs_young/GP_DESeq2_results_Old_vs_Yang.csv" , row.names = 1) %>% mutate(gene_symbol = str_to_title(gene_symbol) )bulk_DE_genes_NSC_Mo7_vs_young <- read.csv(file = "../Bulk_sequencing/DESeq2_results_old_vs_young/GP_DESeq2_results_M7_vs_Young.csv" , row.names = 1) %>% mutate(gene_symbol = str_to_title(gene_symbol) )Neuroblasts
bulk_DE_genes_NB <- read.csv(file = "../Bulk_sequencing/DESeq2_results_old_vs_young/PSA_DESeq2_results_Old_vs_Yang.csv" , row.names = 1) %>% mutate(gene_symbol = str_to_title(gene_symbol) )Microglia
bulk_DE_genes_Microglia <- read.csv(file = "../Bulk_sequencing/DESeq2_results_old_vs_young/CD11b_DESeq2_results_Old_vs_Yang.csv" , row.names = 1) %>% mutate(gene_symbol = str_to_title(gene_symbol) )Endothelial Cells
bulk_DE_genes_Endothelial <- read.csv(file = "../Bulk_sequencing/DESeq2_results_old_vs_young/CD31_DESeq2_results_Old_vs_Yang.csv" , row.names = 1) %>% mutate(gene_symbol = str_to_title(gene_symbol) )Make a list of the differentially expressed genes
bulk_DE_genes <- list( NSC = bulk_DE_genes_NSC , NB = bulk_DE_genes_NB , Microglia = bulk_DE_genes_Microglia , Endothelial = bulk_DE_genes_Endothelial )MA Plots
MAPlot <- function( x , main = "" , highlight_genes = c() , data = NULL , SMARTseq2 = FALSE , labeL_top_genes = FALSE , max_foldChange = 10 , min_baseMean = 0.1 , max_baseMean = 1000000 ){
require(dplyr)
require(ggplot2)
require(ggrepel)
if(!is.null(data)){
rowmean <- rowMeans(data)
rowmean_df <- data.frame(baseMean = as.numeric(rowmean) , gene_symbol = as.character( names(rowmean) ) )
if(SMARTseq2){
colnames(rowmean_df)[2] <- "ensembl_gene_id"
x <- left_join(x = x, y = rowmean_df , by = "ensembl_gene_id" )
}else{
x <- left_join(x = x, y = rowmean_df , by = "gene_symbol" )
}
}
x_sig <- filter( x , padj < 0.05 & abs(log2FoldChange) > 1 )
if( labeL_top_genes ){ x_label <- filter( x_sig , abs(log2FoldChange) > 4 ) }
x_highlight <- filter( x , gene_symbol %in% highlight_genes )
g <- ggplot(data = x , mapping = aes(x = baseMean , y = log2FoldChange )) + geom_point(color = "grey" ) + geom_point(data = x_sig , color = "red" ) + geom_point(data = x_highlight , color = "black", fill = "cyan" , size = 2 , pch = 21)
if( labeL_top_genes ){
g <- g + geom_text_repel(data = x_label , mapping = aes( x = baseMean , y = log2FoldChange , label = gene_symbol))
}
g <- g + scale_x_log10(limits = c(min_baseMean, max_baseMean ), expand = c(0, 0)) + ggtitle(main) + ylim(c(-max_foldChange,max_foldChange))
g
}Bulk
NSC
With Labels
MAPlot(x = bulk_DE_genes_NSC , main = "NSC (19 Month vs 2 Month)" , labeL_top_genes = TRUE )
MAPlot(x = bulk_DE_genes_NSC_Mo7_vs_young, main = "NSC (7 Month vs 2 Month)" , labeL_top_genes = TRUE)
Without Labels
MAPlot(x = bulk_DE_genes_NSC , main = "NSC (19 Month vs 2 Month)" )
MAPlot(x = bulk_DE_genes_NSC_Mo7_vs_young , main = "NSC (7 Month vs 2 Month)")
NB
MAPlot(x = bulk_DE_genes_NB , main = "NB")
MAPlot(x = bulk_DE_genes_NB , main = "NB" , labeL_top_genes = TRUE)
Microglia
MAPlot(x = bulk_DE_genes_Microglia , main = "Microglia")
MAPlot(x = bulk_DE_genes_Microglia , main = "Microglia" , labeL_top_genes = TRUE)
Endothelial cells
MAPlot(x = bulk_DE_genes_Endothelial , main = "Endothelial")
MAPlot(x = bulk_DE_genes_Endothelial , main = "Endothelial" , labeL_top_genes = TRUE)
Heatmaps of gene expression
HeatmapGenes <- function( x , genes = c() , topVar = NULL , main = "" , annotation , annotation_colors , cluster_cols = FALSE , cluster_rows = TRUE , show_rownames = FALSE , show_colnames = FALSE , arrange_by_age = FALSE , keep_non_expressed = FALSE ){
require("pheatmap")
require("viridisLite")
# The cells in the annotation are taken as the cells to be plotted
annotation <- remove_rownames(df = annotation)
if(! arrange_by_age){
annotation <- annotation %>% arrange( type , age)
}else{
annotation <- annotation %>% arrange( age , type)
}
if("cell" %in% colnames(annotation)){ names_cols <- annotation %>% pull("cell") %>% as.character() }
if("sample" %in% colnames(annotation)){ names_cols <- annotation %>% pull("sample") %>% as.character() }
# Then the annotation data.frame is prepared as needed for the pheatmap function with the cell names as rownames
if("cell" %in% colnames(annotation)){annotation <- column_to_rownames(df = annotation , var = "cell")}
if("sample" %in% colnames(annotation)){annotation <- column_to_rownames(df = annotation , var = "sample")}
# We set the order of columns of the annotation to be first age then type
annotation <- annotation[,c("age","type")]
# We check for the genes from the list that are available in our data
genes <- as.character(genes)
genes_avail <- genes[genes %in% rownames(x)]
if(!is.null(topVar)){
x <- x[,names_cols]
var_genes <- names(sort(apply(X = x, MARGIN = 1, FUN = var), decreasing = TRUE)[1:topVar])
var_genes <- var_genes[!is.na(var_genes)]
x_mat <- x[var_genes ,]
}else{
# Only take the values for genes that are from the gene list
x_mat <- x[genes_avail,names_cols]
}
# Dicard all the non expressed genes as they would create problems during the clustering process
if(!keep_non_expressed){
x <- x[apply(X = x, MARGIN = 1, FUN = var) > 0,]
}
if(keep_non_expressed & cluster_rows){warning("Row clustering cannot be used if unexpressed genes are kept!")}
pheatmap(
mat = x_mat ,
cluster_rows = cluster_rows ,
cluster_cols = cluster_cols ,
color = viridis(n = 100),
show_rownames = show_rownames,
show_colnames = show_colnames ,
clustering_method = "complete",
clustering_distance_rows = "euclidean" ,
border_color = NA,
scale = "none",
annotation_col = annotation,
main = main,
fontsize = 5,
annotation_colors = annotation_colors
)
}SMARTseq2 data
Top Variable Genes in subpopulations
qNSC1: Top 5000 variable genes
HeatmapGenes(x = smartseq2_data , topVar = 5000 , main = "SMARTseq2: qNSC1 young and old Top variable genes" , annotation = filter(smartseq2_annotation , type %in% c("qNSC1")) , annotation_colors = list(age = age_colors , type = smartseq2_celltype_colors ) , cluster_cols = TRUE , cluster_rows = TRUE)Loading required package: viridisLite
qNSC2: Top 5000 variable genes
HeatmapGenes(x = smartseq2_data , topVar = 5000 , main = "SMARTseq2: qNSC2 young and old Top variable genes" , annotation = filter(smartseq2_annotation , type %in% c("qNSC2")) , annotation_colors = list(age = age_colors , type = smartseq2_celltype_colors ) , cluster_cols = TRUE , cluster_rows = TRUE)
aNSC1: Top 5000 variable genes
HeatmapGenes(x = smartseq2_data , topVar = 5000 , main = "SMARTseq2: aNSC1 young and old Top variable genes" , annotation = filter(smartseq2_annotation , type %in% c("aNSC1")) , annotation_colors = list(age = age_colors , type = smartseq2_celltype_colors ) , cluster_cols = TRUE , cluster_rows = TRUE)
aNSC2: Top 5000 variable genes
HeatmapGenes(x = smartseq2_data , topVar = 5000 , main = "SMARTseq2: aNSC2 young and old Top variable genes" , annotation = filter(smartseq2_annotation , type %in% c("aNSC2")) , annotation_colors = list(age = age_colors , type = smartseq2_celltype_colors ) , cluster_cols = TRUE , cluster_rows = TRUE)
10X data
Top Variable Genes in subpopulations
qNSC1: Top 5000 variable genes
# HeatmapGenes(x = tenx_data_ensembl , topVar = 5000 , main = "10X: qNSC1 young and old Top variable genes" , annotation = filter(tenx_annotation , type %in% c("qNSC1")) , annotation_colors = list(age = age_colors , type = tenx_celltype_colors ) , cluster_cols = TRUE , cluster_rows = TRUE)qNSC2: Top 5000 variable genes
# HeatmapGenes(x = tenx_data_ensembl , topVar = 5000 , main = "10X: qNSC2 young and old Top variable genes" , annotation = filter(tenx_annotation , type %in% c("qNSC2")) , annotation_colors = list(age = age_colors , type = tenx_celltype_colors ) , cluster_cols = TRUE , cluster_rows = TRUE )aNSC0: Top 5000 variable genes
# HeatmapGenes(x = tenx_data_ensembl , topVar = 5000 , main = "10X: aNSC0 young and old Top variable genes" , annotation = filter(tenx_annotation , type %in% c("aNSC0")) , annotation_colors = list(age = age_colors , type = tenx_celltype_colors ) , cluster_cols = TRUE , cluster_rows = TRUE)aNSC1: Top 5000 variable genes
# HeatmapGenes(x = tenx_data_ensembl , topVar = 5000 , main = "10X: aNSC1 young and old Top variable genes" , annotation = filter(tenx_annotation , type %in% c("aNSC1")) , annotation_colors = list(age = age_colors , type = tenx_celltype_colors ) , cluster_cols = TRUE , cluster_rows = TRUE)aNSC2: Top 5000 variable genes
# HeatmapGenes(x = tenx_data_ensembl , topVar = 5000 , main = "10X: aNSC2 young and old Top variable genes" , annotation = filter(tenx_annotation , type %in% c("aNSC2")) , annotation_colors = list(age = age_colors , type = tenx_celltype_colors ) , cluster_cols = TRUE , cluster_rows = TRUE)TAP: Top 5000 variable genes
# HeatmapGenes(x = tenx_data_ensembl , topVar = 5000 , main = "10X: TAP young and old Top variable genes" , annotation = filter(tenx_annotation , type %in% c("TAP")) , annotation_colors = list(age = age_colors , type = tenx_celltype_colors ) , cluster_cols = TRUE , cluster_rows = TRUE)NB: Top 5000 variable genes
# HeatmapGenes(x = tenx_data_ensembl , topVar = 5000 , main = "10X: NB young and old Top variable genes" , annotation = filter(tenx_annotation , type %in% c("NB")) , annotation_colors = list(age = age_colors , type = tenx_celltype_colors ) , cluster_cols = TRUE , cluster_rows = TRUE)OPC: Top 5000 variable genes
# HeatmapGenes(x = tenx_data_ensembl , topVar = 5000 , main = "10X: OPC young and old Top variable genes" , annotation = filter(tenx_annotation , type %in% c("OPC")) , annotation_colors = list(age = age_colors , type = tenx_celltype_colors ) , cluster_cols = TRUE , cluster_rows = TRUE)OD: Top 5000 variable genes
# HeatmapGenes(x = tenx_data_ensembl , topVar = 5000 , main = "10X: OD young and old Top variable genes" , annotation = filter(tenx_annotation , type %in% c("OD")) , annotation_colors = list(age = age_colors , type = tenx_celltype_colors ) , cluster_cols = TRUE , cluster_rows = TRUE)Compare pseudo-bulk Single Cell RNA-seq with bulk samples - mean TPM values
Pseudobulk_bulk_compare <- function( pseudobulk_data = NULL , pseudobulk_celltype = "x" , bulk_celltype = "y" , bulk_data = NULL , annotation_pseudobulk = NULL , annotation_bulk = NULL , celltype_bulk = NULL , main = "" ){
require("ggplot2")
require("dplyr")
require("tibble")
if( is.null(pseudobulk_data) | is.null(bulk_data) ){ stop("You need to supply all data !") }
if( is.null(annotation_pseudobulk) | is.null(annotation_pseudobulk) ){stop("You need to supply the annotation !")}
## Strip Log2 transform of the data
pseudobulk_data <- 2^(pseudobulk_data)-1
bulk_data <- 2^(bulk_data)-1
pseudobulk_data <- pseudobulk_data[!is.na(rownames(pseudobulk_data)),]
bulk_data <- bulk_data[!is.na(rownames(bulk_data)),]
pseudobulk_data <- pseudobulk_data[,annotation_pseudobulk$cell]
bulk_data <- bulk_data[,annotation_bulk$sample]
pseudobulk_data_comb <- data.frame( expr_x = log2(rowMeans(pseudobulk_data)+1) , ensembl_gene_id = rownames(pseudobulk_data) )
bulk_data_comb <- data.frame( expr_y = log2(rowMeans(bulk_data)+1) , ensembl_gene_id = rownames(bulk_data) )
x <- full_join(x = pseudobulk_data_comb , y = bulk_data_comb , by = "ensembl_gene_id" )
g <- ggplot(data = x , mapping = aes(x = expr_x , y = expr_y)) + geom_point(alpha = 0.5) + coord_equal() + labs(x = pseudobulk_celltype , y = bulk_celltype ) + ggtitle(main)
g
}SMARTseq2
Pseudobulk_bulk_compare(pseudobulk_data = smartseq2_data , bulk_data = bulk_data , annotation_pseudobulk = filter(smartseq2_annotation, type %in% c("qNSC1","qNSC2","aNSC1","aNSC2"), age == "young") , annotation_bulk = filter(bulk_annotation, type %in% c("NSC") , age == "young"), pseudobulk_celltype = "pseudo-bulk samples: NSC (log2(TPM+1))" , bulk_celltype = " bulk samples: NSC (log2(TPM+1))" , main = "Young NSC: pseudo-bulk vs bulk" )Column `ensembl_gene_id` joining factors with different levels, coercing to character vector
Pseudobulk_bulk_compare(pseudobulk_data = smartseq2_data , bulk_data = bulk_data , annotation_pseudobulk = filter(smartseq2_annotation, type %in% c("qNSC1","qNSC2","aNSC1","aNSC2"), age == "old") , annotation_bulk = filter(bulk_annotation, type %in% c("NSC") , age == "young"), pseudobulk_celltype = "pseudo-bulk samples: NSC (log2(TPM+1))" , bulk_celltype = " bulk samples: NSC (log2(TPM+1))" , main = "NSC: old pseudo-bulk (SMARTseq2) vs young bulk" )Column `ensembl_gene_id` joining factors with different levels, coercing to character vector
Pseudobulk_bulk_compare(pseudobulk_data = smartseq2_data , bulk_data = bulk_data , annotation_pseudobulk = filter(smartseq2_annotation, type %in% c("qNSC1","qNSC2","aNSC1","aNSC2"), age == "young") , annotation_bulk = filter(bulk_annotation, type %in% c("NSC") , age == "old"), pseudobulk_celltype = "pseudo-bulk samples: NSC (log2(TPM+1))" , bulk_celltype = " bulk samples: NSC (log2(TPM+1))" , main = "NSC: young pseudo-bulk (SMARTseq2) vs old bulk" )Column `ensembl_gene_id` joining factors with different levels, coercing to character vector
Pseudobulk_bulk_compare(pseudobulk_data = smartseq2_data , bulk_data = bulk_data , annotation_pseudobulk = filter(smartseq2_annotation, type %in% c("qNSC1","qNSC2","aNSC1","aNSC2"), age == "old") , annotation_bulk = filter(bulk_annotation, type %in% c("NSC") , age == "old"), pseudobulk_celltype = "pseudo-bulk samples: NSC (log2(TPM+1))" , bulk_celltype = " bulk samples: NSC (log2(TPM+1))" , main = "Old NSC: pseudo-bulk (SMARTseq2) vs bulk" )Column `ensembl_gene_id` joining factors with different levels, coercing to character vector
10X
Pseudobulk_bulk_compare(pseudobulk_data = tenx_data_ensembl , bulk_data = bulk_data , annotation_pseudobulk = filter(tenx_annotation, type %in% c("qNSC1","qNSC2","aNSC0","aNSC1","aNSC2"), age == "old") , annotation_bulk = filter(bulk_annotation, type %in% c("NSC") , age == "old"), pseudobulk_celltype = "pseudo-bulk samples: NSC (log2(expression+1))" , bulk_celltype = " bulk samples: NSC (log2(TPM+1))" , main = "Old NSC: pseudo-bulk (10X) vs bulk" )Column `ensembl_gene_id` joining factors with different levels, coercing to character vector
Pseudobulk_bulk_compare(pseudobulk_data = tenx_data_ensembl , bulk_data = bulk_data , annotation_pseudobulk = filter(tenx_annotation, type %in% c("qNSC1","qNSC2","aNSC0","aNSC1","aNSC2"), age == "old") , annotation_bulk = filter(bulk_annotation, type %in% c("NSC") , age == "young"), pseudobulk_celltype = "pseudo-bulk samples: NSC (log2(expression+1))" , bulk_celltype = " bulk samples: NSC (log2(TPM+1))" , main = "NSC: Old pseudo-bulk (10X) vs young bulk" )Column `ensembl_gene_id` joining factors with different levels, coercing to character vector
Pseudobulk_bulk_compare(pseudobulk_data = tenx_data_ensembl , bulk_data = bulk_data , annotation_pseudobulk = filter(tenx_annotation, type %in% c("qNSC1","qNSC2","aNSC0","aNSC1","aNSC2"), age == "young") , annotation_bulk = filter(bulk_annotation, type %in% c("NSC") , age == "old"), pseudobulk_celltype = "pseudo-bulk samples: NSC (log2(TPM+1))" , bulk_celltype = " bulk samples: NSC (log2(expression+1))" , main = "NSC: Young pseudo-bulk (10X) vs old bulk" )Column `ensembl_gene_id` joining factors with different levels, coercing to character vector
Pseudobulk_bulk_compare(pseudobulk_data = tenx_data_ensembl , bulk_data = bulk_data , annotation_pseudobulk = filter(tenx_annotation, type %in% c("qNSC1","qNSC2","aNSC0","aNSC1","aNSC2"), age == "young") , annotation_bulk = filter(bulk_annotation, type %in% c("NSC") , age == "young"), pseudobulk_celltype = "pseudo-bulk samples: NSC (log2(TPM+1))" , bulk_celltype = " bulk samples: NSC (log2(expression+1))" , main = "Young NSC: pseudo-bulk (10X) vs bulk" )Column `ensembl_gene_id` joining factors with different levels, coercing to character vector
Pseudobulk MA Plot
Pseudobulk_bulk_MAPlot <- function( pseudobulk_data = NULL , annotation_pseudobulk = NULL , main = "" , labeL_top_genes = FALSE , highlight_genes = c() , max_foldChange = 10 , max_baseMean = 1000000 ){
require("ggplot2")
require("dplyr")
require("tibble")
## Strip Log2 transform of the data
pseudobulk_data <- 2^(pseudobulk_data) - 1
bulk_data <- 2^(bulk_data) - 1
if( is.null(pseudobulk_data) ){ stop("You need to supply all data !") }
if( is.null(annotation_pseudobulk) ){stop("You need to supply the annotation !")}
annotation_pseudobulk_old <- filter(annotation_pseudobulk , age == "old")
annotation_pseudobulk_young <- filter(annotation_pseudobulk , age == "young")
pseudobulk_data <- pseudobulk_data[!is.na(rownames(pseudobulk_data)),]
pseudobulk_data_old <- pseudobulk_data[,as.character(annotation_pseudobulk_old$cell)]
pseudobulk_data_young <- pseudobulk_data[,as.character(annotation_pseudobulk_young$cell)]
pseudobulk_data_complete <- pseudobulk_data[, c( as.character(annotation_pseudobulk_old$cell),as.character(annotation_pseudobulk_young$cell)) ]
if( ! all(rownames(pseudobulk_data_old) == rownames(pseudobulk_data_young))){stop("Rownames are not correct: Pseudobulk")}
pseudobulk_data_comb <- data.frame( log2FoldChange = log2(((rowSums(pseudobulk_data_old) + 1) / (rowSums(pseudobulk_data_young) + 1) ) ) , expr_young_pseudobulk = rowSums(pseudobulk_data_young) , baseMean = rowMeans(pseudobulk_data_complete), expr_old_pseudobulk = rowSums(pseudobulk_data_old) , gene = rownames(pseudobulk_data_old) )
pseudobulk_data_comb$log2FoldChange[is.nan(pseudobulk_data_comb$log2FoldChange)] <- 0
x_highlight <- filter(pseudobulk_data_comb ,gene %in% highlight_genes )
g <- ggplot(data = pseudobulk_data_comb , mapping = aes(x = baseMean , y = log2FoldChange)) + labs(x = "baseMean" , y = "Log2FoldChange" ) + ggtitle(main) + geom_vline(xintercept = 0) + geom_hline(yintercept = 0) + geom_point(color = "grey" ) + geom_point(data = x_highlight , color = "black", fill = "cyan" , size = 2 , pch = 21)
if( labeL_top_genes ){
g <- g + geom_text_repel(data = x_label , mapping = aes( x = baseMean , y = log2FoldChange , label = gene))
}
g <- g + scale_x_log10(limits = c(0.00001, max_baseMean ), expand = c(0, 0)) + ggtitle(main) + ylim(c(-max_foldChange,max_foldChange))
g
}Pseudobulk_bulk_MAPlot(pseudobulk_data = smartseq2_data , annotation_pseudobulk = smartseq2_annotation)
Pseudobulk_bulk_MAPlot(pseudobulk_data = tenx_data , annotation_pseudobulk = dplyr::filter(tenx_annotation, type %in% c("qNSC1","qNSC2","aNSC0","aNSC1","aNSC2")) )
Plot individual genes in the single cell datasets
Beeswarm plot of Il33
SMARTseq2
IL33_smartseq2_data <- smartseq2_data["ENSMUSG00000024810",]IL33_smartseq2_df <- data.frame( expression = IL33_smartseq2_data , row.names = names(IL33_smartseq2_data) ) %>% rownames_to_column("cell") %>% left_join( smartseq2_annotation , by = "cell")Column `cell` joining character vector and factor, coercing into character vectoraddmargins( table( IL33_smartseq2_df$age , IL33_smartseq2_df$expression > 2.5 ) )
FALSE TRUE Sum
young 73 16 89
old 71 62 133
Sum 144 78 222library(ggbeeswarm)
ggplot(data = IL33_smartseq2_df , mapping = aes(x = age , y = expression , color = type )) + geom_quasirandom( size = 1.5 ,method = "smiley" ) + xlab("Age") + ylab("log2(TPM+1)") + ggtitle("Expression of Il33 in the SMARTseq2 data") + scale_x_discrete(labels = c(young = "2 month" , old = "22 month") ) + scale_color_manual( name = "Activation state" , values = smartseq2_celltype_colors ) + guides(color = guide_legend(override.aes=list(size=1.5)))
10X
IL33_10X_data <- tenx_data_ensembl["ENSMUSG00000024810",]tenx_annotation_nscs <- tenx_annotation %>% filter( type %in% c("qNSC1","qNSC2","aNSC0","aNSC1","aNSC2"))
IL33_10X_df <- data.frame( expression = IL33_10X_data , row.names = names(IL33_10X_data) ) %>% rownames_to_column("cell") %>% filter(cell %in% as.character( tenx_annotation_nscs$cell ) ) %>% left_join( tenx_annotation_nscs , by = "cell")addmargins( table( IL33_10X_df$age , IL33_10X_df$expression > 0.5 ) )
FALSE TRUE Sum
young 1162 63 1225
old 836 185 1021
Sum 1998 248 2246library(ggbeeswarm)
ggplot(data = IL33_10X_df , mapping = aes(x = age , y = expression , color = type )) + xlab("Age") + ylab("log2(expression+1)") + ggtitle("Expression of Il33 in the 10X data") + scale_x_discrete(labels = c(young = "2 month" , old = "22 month") ) + scale_color_manual( name = "Activation state" , values = tenx_celltype_colors ) + guides(color = guide_legend(override.aes=list(size=1.5))) + geom_quasirandom( size = 0.5 ,method = "smiley" ) 
Search for genes from Gene Lists in all datasets
InnateDB genes - manually curated from the bulk seq data (from sig. genes with log2FoldChange > 1)
DE_genes_NSCs_bulk_up_InnateDB <- read.csv("GeneLists/Manually_curated_DE_genes_from_bulk/InnateDB_Gene_list/InnateDB_NSCs.csv" , stringsAsFactors = FALSE , header = TRUE)
library(biomaRt)
mart <- useMart(biomart = "ensembl", dataset = "mmusculus_gene_ensembl")
genes_symbol_ensembl <- getBM(attributes = c("mgi_symbol","ensembl_gene_id" ), filters = "mgi_symbol" , values = DE_genes_NSCs_bulk_up_InnateDB$Genes , mart = mart )
genes_symbol_ensembl <- dplyr::rename(genes_symbol_ensembl , MGI.symbol = mgi_symbol , gene = ensembl_gene_id )
genes_symbol_ensembl <- dplyr::filter( genes_symbol_ensembl , gene %in% as.character(bulk_DE_genes_NSC %>% filter(padj < 0.05 , log2FoldChange > 1) %>% pull("ensembl_gene_id") ) )
DE_genes_NSCs_bulk_up_InnateDB <- genes_symbol_ensemblgene_set <- DE_genes_NSCs_bulk_up_InnateDBplot_title <- "NSC bulk seq (top DE genes between old and young) annotated in InnateDB"Bulk
Bulk sequencing: Heatmap
HeatmapGenes(x = bulk_data , genes = unique(gene_set$gene) , main = paste("Bulk:", plot_title) , annotation = bulk_annotation , show_colnames = FALSE , annotation_colors = list(age = age_colors_bulk ) )
Bulk sequencing: MA-Plot
NSC
MAPlot(x = bulk_DE_genes_NSC , main = "NSC (19 month vs 2 month)" , highlight_genes = unique(gene_set$MGI.symbol) ) 
MAPlot(x = bulk_DE_genes_NSC_Mo7_vs_young , main = "NSC (7 Month vs 2 Month)" , highlight_genes = unique(gene_set$MGI.symbol) ) 
NB
MAPlot(x = bulk_DE_genes_NB , main = "NB" , highlight_genes = unique(gene_set$MGI.symbol) ) 
Microglia
MAPlot(x = bulk_DE_genes_Microglia , main = "Microglia" , highlight_genes = unique(gene_set$MGI.symbol) ) 
Endothelial cells
MAPlot(x = bulk_DE_genes_Endothelial , main = "Endothelial" , highlight_genes = unique(gene_set$MGI.symbol) ) 
Pseudobulk SMARTseq2
Pseudobulk_bulk_MAPlot(pseudobulk_data = smartseq2_data , annotation_pseudobulk = smartseq2_annotation , highlight_genes = unique(gene_set$gene) , main = "Pseudobulk: SMARTseq2")
Pseudobulk 10X
Pseudobulk_bulk_MAPlot(pseudobulk_data = tenx_data , annotation_pseudobulk = dplyr::filter(tenx_annotation, type %in% c("qNSC1","qNSC2","aNSC0","aNSC1","aNSC2")) , highlight_genes = unique(gene_set$MGI.symbol) , main = "Pseudobulk: 10X")
SMARTseq2
SMARTseq2 sequencing: Heatmap
Arranged by celltype
HeatmapGenes(x = smartseq2_data , genes = unique(gene_set$gene) , main = paste("SMARTseq2:", plot_title) , annotation = smartseq2_annotation , annotation_colors = list(age = age_colors , type = smartseq2_celltype_colors ) )
Columns clustered
test <- HeatmapGenes(x = smartseq2_data , genes = unique(gene_set$gene) , main = paste("SMARTseq2:", plot_title) , annotation = smartseq2_annotation %>% dplyr::filter(type %in% c("qNSC1","qNSC2")) , annotation_colors = list(age = age_colors , type = smartseq2_celltype_colors ) , cluster_cols = TRUE )
10X
10X sequencing: Heatmap
HeatmapGenes(x = tenx_data_ensembl , genes = unique(gene_set$gene) , main = paste("10X:", plot_title) , annotation = tenx_annotation , annotation_colors = list(age = age_colors , type = tenx_celltype_colors ) )
HeatmapGenes(x = tenx_data_ensembl , genes = unique(gene_set$gene) , main = paste("10X:", plot_title) , annotation = tenx_annotation , annotation_colors = list(age = age_colors , type = tenx_celltype_colors ) , cluster_cols = TRUE )
InnateDB genes - manually curated from the bulk seq data (from sig. genes with log2FoldChange > 1) - from Endothelial, NB and Microglia
Endothelial
DE_genes_Endothelial_bulk_up_InnateDB <- read.csv("GeneLists/Manually_curated_DE_genes_from_bulk/InnateDB_Gene_list/InnateDB_Endos.csv" , stringsAsFactors = FALSE , header = TRUE)
library(biomaRt)
mart <- useMart(biomart = "ensembl", dataset = "mmusculus_gene_ensembl")
genes_symbol_ensembl <- getBM(attributes = c("mgi_symbol","ensembl_gene_id" ), filters = "mgi_symbol" , values = DE_genes_Endothelial_bulk_up_InnateDB$Genes , mart = mart )
genes_symbol_ensembl <- dplyr::rename(genes_symbol_ensembl , MGI.symbol = mgi_symbol , gene = ensembl_gene_id )
genes_symbol_ensembl <- dplyr::filter( genes_symbol_ensembl , gene %in% as.character(bulk_DE_genes_Endothelial %>% filter(padj < 0.05 , log2FoldChange > 1) %>% pull("ensembl_gene_id") ) )
DE_genes_Endothelial_bulk_up_InnateDB <- genes_symbol_ensemblMAPlot(x = bulk_DE_genes_Endothelial , main = "Endothelial" , highlight_genes = unique(DE_genes_Endothelial_bulk_up_InnateDB$MGI.symbol) ) 
Microglia
DE_genes_Microglia_bulk_up_InnateDB <- read.csv("GeneLists/Manually_curated_DE_genes_from_bulk/InnateDB_Gene_list/InnateDB_Microglia.csv" , stringsAsFactors = FALSE , header = TRUE)
library(biomaRt)
mart <- useMart(biomart = "ensembl", dataset = "mmusculus_gene_ensembl")
genes_symbol_ensembl <- getBM(attributes = c("mgi_symbol","ensembl_gene_id" ), filters = "mgi_symbol" , values = DE_genes_Microglia_bulk_up_InnateDB$Genes , mart = mart )
genes_symbol_ensembl <- dplyr::rename(genes_symbol_ensembl , MGI.symbol = mgi_symbol , gene = ensembl_gene_id )
genes_symbol_ensembl <- dplyr::filter( genes_symbol_ensembl , gene %in% as.character(bulk_DE_genes_Microglia %>% dplyr::filter(padj < 0.05 , log2FoldChange > 1) %>% pull("ensembl_gene_id") ) )
DE_genes_Microglia_bulk_up_InnateDB <- genes_symbol_ensemblMAPlot(x = bulk_DE_genes_Microglia , main = "Microglia" , highlight_genes = unique(DE_genes_Microglia_bulk_up_InnateDB$MGI.symbol) ) 
NB
DE_genes_NB_bulk_up_InnateDB <- read.csv("GeneLists/Manually_curated_DE_genes_from_bulk/InnateDB_Gene_list/InnateDB_Neuroblasts.csv" , stringsAsFactors = FALSE , header = TRUE)
library(biomaRt)
mart <- useMart(biomart = "ensembl", dataset = "mmusculus_gene_ensembl")
genes_symbol_ensembl <- getBM(attributes = c("mgi_symbol","ensembl_gene_id" ), filters = "mgi_symbol" , values = DE_genes_NB_bulk_up_InnateDB$Genes , mart = mart )
genes_symbol_ensembl <- dplyr::rename(genes_symbol_ensembl , MGI.symbol = mgi_symbol , gene = ensembl_gene_id )
genes_symbol_ensembl <- dplyr::filter( genes_symbol_ensembl , gene %in% as.character(bulk_DE_genes_NB %>% dplyr::filter(padj < 0.05 , log2FoldChange > 1) %>% pull("ensembl_gene_id") ) )
DE_genes_NB_bulk_up_InnateDB <- genes_symbol_ensemblMAPlot(x = bulk_DE_genes_NB , main = "Neuroblast" , highlight_genes = unique(DE_genes_NB_bulk_up_InnateDB$MGI.symbol) ) 
Load raw counts
We can also compare the raw counts for specific genes that we recover with the different sequencing methods
Bulk sequencing data
data_bulk <- read.csv(file = "raw_counts/compatible_rownames_only_NSCs/Bulk_seq_All.genes.counts_compat.csv" , row.names = 1)
anno_bulk <- read.csv(file = "raw_counts/annotation_bulk.csv" , row.names = 1)Check if all cells from the annotation are present in the data as columns and the other way around
all(colnames(data_bulk) == anno_bulk$name)[1] TRUESMARTseq2 data
data_SMARTseq2_NSCs <- read.csv(file = "raw_counts/compatible_rownames_only_NSCs/SMARTseq2_All.genes.counts_compat.csv" , row.names = 1)
anno_SMARTseq2_NSCs <- read.csv(file = "raw_counts/annotation_NSCs_SMARTseq2.csv" , row.names = 1)Check if all cells from the annotation are present in the data as columns and the other way around
all(colnames(data_SMARTseq2_NSCs) == anno_SMARTseq2_NSCs$name)[1] TRUE10X data
data_10X_NSCs <- read.csv(file = "raw_counts/compatible_rownames_only_NSCs/10X_All.genes.counts_compat.csv" , row.names = 1)
anno_10X_NSCs <- read.csv(file = "raw_counts/annotation_10x_NSCs.csv" , row.names = 1)colnames(data_10X_NSCs) <- sub(x = colnames(data_10X_NSCs) , pattern = "\\." , replacement = "-" )Check if all cells from the annotation are present in the data as columns and the other way around
all(colnames(data_10X_NSCs) == anno_10X_NSCs$name)[1] TRUECheck raw counts for highly upregulated genes in bulk NSC old to young comparison
10X
How many counts do genes have in the Single cell data sets, that are significantly upregulated in the bulk NSCs
cells_NSCs_10X <- as.character(anno_10X_NSCs$name)Iigp1 - ENSMUSG00000054072
Iigp1_df <- data.frame( totalReads = colSums(data_10X_NSCs) , geneReads = as.numeric(data_10X_NSCs["ENSMUSG00000054072",cells_NSCs_10X]) , gene = "Iigp1" ) %>% rownames_to_column("name") %>% left_join(anno_10X_NSCs , by = "name" )Column `name` joining character vector and factor, coercing into character vectoraddmargins( table( Iigp1_df$age , Iigp1_df$geneReads > 0 ) )
FALSE TRUE Sum
old 996 25 1021
young 1221 4 1225
Sum 2217 29 2246gg_Iigp1 <- ggplot(data = Iigp1_df , mapping = aes(x = totalReads , y = geneReads ) ) + geom_point(aes(color = age)) + scale_color_manual(values = c("old" = "slateblue" , "young" = "yellowgreen") )
gg_Iigp1
Ddx60 - ENSMUSG00000037921
Ddx60_df <- data.frame( totalReads = colSums(data_10X_NSCs) , geneReads = as.numeric(data_10X_NSCs["ENSMUSG00000037921",cells_NSCs_10X]) , gene = "Ddx60" ) %>% rownames_to_column("name") %>% left_join(anno_10X_NSCs , by = "name" )Column `name` joining character vector and factor, coercing into character vectoraddmargins( table( Ddx60_df$age , Ddx60_df$geneReads > 0 ) )
FALSE TRUE Sum
old 1002 19 1021
young 1224 1 1225
Sum 2226 20 2246gg_Ddx60 <- ggplot(data = Ddx60_df , mapping = aes(x = totalReads , y = geneReads ) ) + geom_point(aes(color = age)) + scale_color_manual(values = c("old" = "slateblue" , "young" = "yellowgreen") )Ifit1 - ENSMUSG00000078920
Ifit1_df <- data.frame( totalReads = colSums(data_10X_NSCs) , geneReads = as.numeric(data_10X_NSCs["ENSMUSG00000078920",cells_NSCs_10X]) , gene = "Ifit1" ) %>% rownames_to_column("name") %>% left_join(anno_10X_NSCs , by = "name" )Column `name` joining character vector and factor, coercing into character vectoraddmargins( table( Ifit1_df$age , Ifit1_df$geneReads > 0 ) )
FALSE TRUE Sum
old 1017 4 1021
young 1223 2 1225
Sum 2240 6 2246gg_Ifit1 <- ggplot(data = Ifit1_df , mapping = aes(x = totalReads , y = geneReads ) ) + geom_point(aes(color = age)) + scale_color_manual(values = c("old" = "slateblue" , "young" = "yellowgreen") )
gg_Ifit1
Rsad2 - ENSMUSG00000020641
Rsad2_df <- data.frame( totalReads = colSums(data_10X_NSCs) , geneReads = as.numeric(data_10X_NSCs["ENSMUSG00000020641",cells_NSCs_10X]) , gene = "Rsad2" ) %>% rownames_to_column("name") %>% left_join(anno_10X_NSCs , by = "name" )Column `name` joining character vector and factor, coercing into character vectoraddmargins( table( Rsad2_df$age , Rsad2_df$geneReads > 0 ) )
FALSE TRUE Sum
old 995 26 1021
young 1213 12 1225
Sum 2208 38 2246gg_Rsad2 <- ggplot(data = Rsad2_df , mapping = aes(x = totalReads , y = geneReads ) ) + geom_point(aes(color = age)) + scale_color_manual(values = c("old" = "slateblue" , "young" = "yellowgreen") )
gg_Rsad2
C3 - ENSMUSG00000024164
C3_df <- data.frame( totalReads = colSums(data_10X_NSCs) , geneReads = as.numeric(data_10X_NSCs["ENSMUSG00000024164",cells_NSCs_10X]) , gene = "C3" ) %>% rownames_to_column("name") %>% left_join(anno_10X_NSCs , by = "name" )Column `name` joining character vector and factor, coercing into character vectoraddmargins( table( C3_df$age , C3_df$geneReads > 0 ) )
FALSE Sum
old 1021 1021
young 1225 1225
Sum 2246 2246gg_C3 <- ggplot(data = C3_df , mapping = aes(x = totalReads , y = geneReads ) ) + geom_point(aes(color = age)) + scale_color_manual(values = c("old" = "slateblue" , "young" = "yellowgreen") )
gg_C3
Ifi44 - ENSMUSG00000028037
Ifi44_df <- data.frame( totalReads = colSums(data_10X_NSCs) , geneReads = as.numeric(data_10X_NSCs["ENSMUSG00000028037",cells_NSCs_10X]) , gene = "Ifi44" ) %>% rownames_to_column("name") %>% left_join(anno_10X_NSCs , by = "name" )Column `name` joining character vector and factor, coercing into character vectoraddmargins( table( Ifi44_df$age , Ifi44_df$geneReads > 0 ) )
FALSE TRUE Sum
old 1019 2 1021
young 1223 2 1225
Sum 2242 4 2246gg_Ifi44 <- ggplot(data = Ifi44_df , mapping = aes(x = totalReads , y = geneReads ) ) + geom_point(aes(color = age)) + scale_color_manual(values = c("old" = "slateblue" , "young" = "yellowgreen") )
gg_Ifi44
Cxcl10 - ENSMUSG00000034855
Cxcl10_df <- data.frame( totalReads = colSums(data_10X_NSCs) , geneReads = as.numeric(data_10X_NSCs["ENSMUSG00000034855",cells_NSCs_10X]) , gene = "Cxcl10" ) %>% rownames_to_column("name") %>% left_join(anno_10X_NSCs , by = "name" )Column `name` joining character vector and factor, coercing into character vectoraddmargins( table( Cxcl10_df$age , Cxcl10_df$geneReads > 0 ) )
FALSE TRUE Sum
old 954 67 1021
young 1154 71 1225
Sum 2108 138 2246gg_Cxcl10 <- ggplot(data = Cxcl10_df , mapping = aes(x = totalReads , y = geneReads ) ) + geom_point(aes(color = age)) + scale_color_manual(values = c("old" = "slateblue" , "young" = "yellowgreen") )
gg_Cxcl10
Il33 - ENSMUSG00000024810
Il33_df <- data.frame( totalReads = colSums(data_10X_NSCs) , geneReads = as.numeric(data_10X_NSCs["ENSMUSG00000024810",cells_NSCs_10X]) , gene = "Il33" ) %>% rownames_to_column("name") %>% left_join(anno_10X_NSCs , by = "name" )Column `name` joining character vector and factor, coercing into character vectoraddmargins( table( Il33_df$age , Il33_df$geneReads > 0 ) )
FALSE TRUE Sum
old 836 185 1021
young 1162 63 1225
Sum 1998 248 2246gg_Il33 <- ggplot(data = Il33_df , mapping = aes(x = totalReads , y = geneReads ) ) + geom_point(aes(color = age)) + scale_color_manual(values = c("old" = "slateblue" , "young" = "yellowgreen") )
gg_Il33
Ifi47 - ENSMUSG00000078920
Ifi47_df <- data.frame( totalReads = colSums(data_10X_NSCs) , geneReads = as.numeric(data_10X_NSCs["ENSMUSG00000078920",cells_NSCs_10X]) , gene = "Ifi47" ) %>% rownames_to_column("name") %>% left_join(anno_10X_NSCs , by = "name" )Column `name` joining character vector and factor, coercing into character vectoraddmargins( table( Ifi47_df$age , Ifi47_df$geneReads > 0 ) )
FALSE TRUE Sum
old 1017 4 1021
young 1223 2 1225
Sum 2240 6 2246gg_Ifi47 <- ggplot(data = Ifi47_df , mapping = aes(x = totalReads , y = geneReads ) ) + geom_point(aes(color = age)) + scale_color_manual(values = c("old" = "slateblue" , "young" = "yellowgreen") )
gg_Ifi47
Gm4951 - ENSMUSG00000073555
Gm4951_df <- data.frame( totalReads = colSums(data_10X_NSCs) , geneReads = as.numeric(data_10X_NSCs["ENSMUSG00000073555",cells_NSCs_10X]) , gene = "Gm4951" ) %>% rownames_to_column("name") %>% left_join(anno_10X_NSCs , by = "name" )Column `name` joining character vector and factor, coercing into character vectoraddmargins( table( Gm4951_df$age , Gm4951_df$geneReads > 0 ) )
FALSE TRUE Sum
old 993 28 1021
young 1223 2 1225
Sum 2216 30 2246gg_Gm4951 <- ggplot(data = Gm4951_df , mapping = aes(x = totalReads , y = geneReads ) ) + geom_point(aes(color = age)) + scale_color_manual(values = c("old" = "slateblue" , "young" = "yellowgreen") )
gg_Gm4951
Slc1a3 - ENSMUSG00000005360
Slc1a3_df <- data.frame( totalReads = colSums(data_10X_NSCs) , geneReads = as.numeric(data_10X_NSCs["ENSMUSG00000005360",cells_NSCs_10X]) , gene = "Slc1a3" ) %>% rownames_to_column("name") %>% left_join(anno_10X_NSCs , by = "name" )Column `name` joining character vector and factor, coercing into character vectoraddmargins( table( Slc1a3_df$age , Slc1a3_df$geneReads > 0 ) )
FALSE TRUE Sum
old 26 995 1021
young 48 1177 1225
Sum 74 2172 2246gg_Slc1a3 <- ggplot(data = Slc1a3_df , mapping = aes(x = totalReads , y = geneReads ) ) + geom_point(aes(color = age)) + scale_color_manual(values = c("old" = "slateblue" , "young" = "yellowgreen") )
gg_Slc1a3
Sox2 - ENSMUSG00000074637
Sox2_df <- data.frame( totalReads = colSums(data_10X_NSCs) , geneReads = as.numeric(data_10X_NSCs["ENSMUSG00000074637",cells_NSCs_10X]) , gene = "Sox2" ) %>% rownames_to_column("name") %>% left_join(anno_10X_NSCs , by = "name" )Column `name` joining character vector and factor, coercing into character vectoraddmargins( table( Sox2_df$age , Sox2_df$geneReads > 0 ) )
FALSE TRUE Sum
old 209 812 1021
young 202 1023 1225
Sum 411 1835 2246gg_Sox2 <- ggplot(data = Sox2_df , mapping = aes(x = totalReads , y = geneReads ) ) + geom_point(aes(color = age)) + scale_color_manual(values = c("old" = "slateblue" , "young" = "yellowgreen") )
gg_Sox2
interferon_genes_bulk_df <- bind_rows( Ifit1_df , C3_df , Rsad2_df , Iigp1_df , Ifi47_df , Il33_df , Cxcl10_df , Sox2_df , Slc1a3_df )Unequal factor levels: coercing to characterbinding character and factor vector, coercing into character vectorbinding character and factor vector, coercing into character vectorbinding character and factor vector, coercing into character vectorbinding character and factor vector, coercing into character vectorbinding character and factor vector, coercing into character vectorbinding character and factor vector, coercing into character vectorbinding character and factor vector, coercing into character vectorbinding character and factor vector, coercing into character vectorbinding character and factor vector, coercing into character vectorCombine all genes
Scatterplot
gg_interferon_genes <- ggplot(data = interferon_genes_bulk_df , mapping = aes(x = totalReads , y = geneReads ) ) + geom_point(aes(color = age)) + scale_color_manual(values = c("old" = "slateblue" , "young" = "yellowgreen") ) + facet_grid( gene ~ . )gg_interferon_genes
Barplot
Show the same data as stacked barplots
interferon_genes_bulk_df_bars <- interferon_genes_bulk_df %>% mutate( interval = base::cut( geneReads , breaks = c(-0.1, 0.5,5.5,50.5,Inf) , labels = c("0","1-5","5-50","50-") ) ) %>% group_by(interval,gene,age) %>% summarise( counts = n() ) %>% ungroup() %>% mutate( interval = fct_rev( interval ) , age = fct_recode( .f = age , "22 month" = "old", "2 month" = "young" ) %>% fct_rev() ) %>% group_by(gene,age) %>% mutate( Percent = 100*counts/sum(counts) )ggplot( data = interferon_genes_bulk_df_bars , mapping = aes(x = age , y = Percent , fill = interval) ) + geom_bar(stat = "identity") + facet_grid(facets = . ~ gene ) + viridis::scale_fill_viridis( discrete = TRUE , direction = -1 ) + ggtitle("Read counts: 10X")
Bulk NSC genes in InnateDB
Which genes are the ones with high fold changes in the bulk and are annotated in InnateDB
innateDB_genes_NSCs_bulk_up_table <- structure(list(ensembl_gene_id = structure(c(24L, 25L, 9L, 40L,
34L, 15L, 17L, 31L, 22L, 30L, 26L, 20L, 19L, 33L, 27L, 4L, 16L,
8L, 7L, 29L, 35L, 1L, 12L, 2L, 32L, 13L, 36L, 10L, 3L, 28L, 21L,
39L, 23L, 11L, 14L, 6L, 5L, 18L, 38L, 37L), .Label = c("ENSMUSG00000000386",
"ENSMUSG00000002325", "ENSMUSG00000005413", "ENSMUSG00000014361",
"ENSMUSG00000015766", "ENSMUSG00000017716", "ENSMUSG00000018899",
"ENSMUSG00000020122", "ENSMUSG00000020641", "ENSMUSG00000021508",
"ENSMUSG00000022306", "ENSMUSG00000023951", "ENSMUSG00000024066",
"ENSMUSG00000024079", "ENSMUSG00000024164", "ENSMUSG00000025492",
"ENSMUSG00000025498", "ENSMUSG00000026104", "ENSMUSG00000026896",
"ENSMUSG00000028270", "ENSMUSG00000029167", "ENSMUSG00000029826",
"ENSMUSG00000030341", "ENSMUSG00000034459", "ENSMUSG00000034855",
"ENSMUSG00000035692", "ENSMUSG00000036905", "ENSMUSG00000036913",
"ENSMUSG00000037860", "ENSMUSG00000038418", "ENSMUSG00000038642",
"ENSMUSG00000040747", "ENSMUSG00000046688", "ENSMUSG00000046718",
"ENSMUSG00000053113", "ENSMUSG00000054717", "ENSMUSG00000060441",
"ENSMUSG00000064215", "ENSMUSG00000074151", "ENSMUSG00000074896"
), class = "factor"), baseMean = c(1424.95976205735, 146.693384980886,
254.747257624993, 1378.74240836277, 232.679222864689, 53.3856147003376,
127.295243864467, 100.858827865452, 1254.48226869821, 8484.55808366433,
133.391584778487, 449.698563109268, 435.970338794981, 487.652611748752,
38.1324871848669, 2270.44585158928, 392.455793416614, 8123.4459446361,
495.513923324155, 147.634742373719, 285.437837120322, 20.0485104704833,
243.691996897216, 513.924995107202, 17.4190622356759, 18.1436595206331,
15890.0096695135, 1952.66573537053, 149.127545170108, 284.046380761723,
1257.30518127357, 52.8851930781209, 761.966603230871, 315.283958297394,
633.932254776546, 1091.87572537937, 450.61462668542, 1757.25022917456,
2425.56654256373, 64.6090952815893), log2FoldChange = c(4.55242859938374,
6.95678411131362, 5.03641775134329, 3.37966257532159, 3.73428610778296,
5.53141829606081, 3.86121962444797, 3.75531459127234, 2.42356052609584,
2.16374238670928, 3.1971506351276, 2.44063114014779, 2.44677858670807,
2.0893762261114, 3.67190586863129, 1.88755481055364, 2.10038800171168,
1.69845072572928, 1.91183744819457, 2.41403420088619, 2.0284929496982,
3.7014584495119, 1.94105240967367, 1.98333192338338, 3.4448344969081,
3.44363682239329, 1.57281037684989, 1.63883482734508, 2.11717369111306,
1.8668146256893, 1.43386187193389, 2.76038968668686, 1.7580616439951,
1.52810896028218, 1.55394451633379, 1.28056799567681, 1.42242513375296,
1.26735536508083, 1.18570808766668, 2.12810319958397), lfcSE = c(0.519384243203232,
0.81388488255093, 0.641951444263734, 0.48612175786269, 0.578514935508735,
0.878589568240684, 0.670578673894941, 0.684535958284751, 0.448804291965827,
0.41379046101385, 0.645816741770005, 0.504575112575078, 0.531966043635904,
0.460946382208802, 0.845572909706191, 0.436183111157877, 0.505011402058278,
0.415807705611759, 0.488183380722589, 0.618808403224005, 0.520676205372646,
0.965738215947367, 0.521153307010798, 0.553367834874403, 0.973891191367301,
0.97448322263972, 0.448895240692065, 0.468806743609123, 0.606652875776758,
0.541455272040843, 0.42118712809647, 0.818497304894856, 0.535426580260121,
0.488159657804439, 0.500868295142623, 0.422276770181993, 0.470630833485906,
0.427622347529472, 0.406142195700318, 0.7298433146452), stat = c(8.76504949651004,
8.54762664900375, 7.84548083246336, 6.95229645795079, 6.45495194432465,
6.29579327596285, 5.75804118854651, 5.48592743130992, 5.40003865711778,
5.2290774934922, 4.95055397041132, 4.83700261729543, 4.59950144558989,
4.53279666953747, 4.34250651420131, 4.32743671698567, 4.1590902564796,
4.08470238239196, 3.91622804808462, 3.90110119434225, 3.89588179518673,
3.83277619999832, 3.7245324620639, 3.58411132413133, 3.53718621489091,
3.53380821997635, 3.50373591492099, 3.49575779292008, 3.48992607741657,
3.44777255312879, 3.40433450189738, 3.37250919481214, 3.28347846149326,
3.13034667214217, 3.10250125912102, 3.03253241973247, 3.02237981990518,
2.96372575568792, 2.91944077768661, 2.91583571005033), pvalue = c(1.86694618820708e-18,
1.25645385522089e-17, 4.31297720037574e-15, 3.59387157612209e-12,
1.08253248479473e-10, 3.05831732678035e-10, 8.50955946071268e-09,
4.11305732780317e-08, 6.66265384184588e-08, 1.70357951680874e-07,
7.40025308679816e-07, 1.31811693167268e-06, 4.23503254113743e-06,
5.82078401153992e-06, 1.40866307399552e-05, 1.50854667946568e-05,
3.19517635376871e-05, 4.41333464318532e-05, 8.99451714694149e-05,
9.57561097095347e-05, 9.78421013854258e-05, 0.000126705214745366,
0.000195677532925451, 0.000338227814291896, 0.000404414420179005,
0.000409618180066453, 0.0004587800815838, 0.000472717577863198,
0.000483154175333289, 0.000565229800758029, 0.000663254779320521,
0.000744865993665538, 0.00102534479496883, 0.00174600119776919,
0.00191892731640661, 0.00242511046065225, 0.00250795646620197,
0.00303939026229686, 0.00350660015484411, 0.00354737241964209
), padj = c(2.76849450249227e-15, 1.43322724762081e-14, 3.55317438357621e-12,
1.15855481744162e-09, 2.43225366924561e-08, 5.96734047879288e-08,
1.10691453721849e-06, 4.09345819557002e-06, 6.02442035492272e-06,
1.40346559193094e-05, 5.05706695963733e-05, 7.72583240307279e-05,
0.00020864218455989, 0.000272291501915222, 0.000555143310344139,
0.00058560834318839, 0.00109173433525429, 0.00143520481192533,
0.00261017015209384, 0.00274124971212874, 0.00278483785306042,
0.00341326958812737, 0.00486047258919852, 0.007542225952082,
0.00869139338671661, 0.00873989639166249, 0.00959555688266032,
0.00981782767805792, 0.00993716125661212, 0.0113267469127579,
0.0128318926960938, 0.0140529488804914, 0.0180028284113852, 0.0273477266839752,
0.0294878478497343, 0.034989937779684, 0.0357257314479433, 0.0415793911120156,
0.0457740965635417, 0.0462249434190444), gene_symbol = c("Ifit1",
"Cxcl10", "Rsad2", "Ifit3", "Bst2", "C3", "Irf7", "Ctss", "Zc3hav1",
"Egr1", "Isg15", "Gbp2", "Ifih1", "Tifa", "C1qb", "Mertk", "Ifitm3",
"Egfr", "Irf1", "Aim2", "Socs3", "Mx1", "Vegfa", "Irf9", "Cd53",
"Xdh", "Hmgb2", "Cxcl14", "Hmox1", "Trim67", "Ppargc1a", "Nlrc5",
"Tnfrsf1a", "Zfpm2", "Eif2ak2", "Birc5", "Eps8", "Stat1", "Ifi27",
"Trim5"), entrezgene = c(15957L, 15945L, 58185L, 15959L, 69550L,
102642251L, 54123L, 13040L, 78781L, 13653L, 100038882L, 14469L,
71586L, 211550L, 12260L, 17289L, 66141L, 13649L, 16362L, 383619L,
12702L, NA, 22339L, 16391L, 12508L, 22436L, 97165L, 57266L, 15368L,
330863L, 19017L, 434341L, 21937L, 22762L, 19106L, 11799L, 13860L,
20846L, 52668L, 667823L), description = structure(c(19L, 6L,
30L, 20L, 3L, 10L, 24L, 4L, 39L, 11L, 26L, 15L, 22L, 33L, 9L,
8L, 21L, 13L, 23L, 1L, 32L, 27L, 37L, 25L, 5L, 38L, 17L, 7L,
16L, 35L, 29L, 28L, 36L, 40L, 14L, 2L, 12L, 31L, 18L, 34L), .Label = c("absent in melanoma 2 [Source:MGI Symbol;Acc:MGI:2686159]",
"baculoviral IAP repeat-containing 5 [Source:MGI Symbol;Acc:MGI:1203517]",
"bone marrow stromal cell antigen 2 [Source:MGI Symbol;Acc:MGI:1916800]",
"cathepsin S [Source:MGI Symbol;Acc:MGI:107341]", "CD53 antigen [Source:MGI Symbol;Acc:MGI:88341]",
"chemokine (C-X-C motif) ligand 10 [Source:MGI Symbol;Acc:MGI:1352450]",
"chemokine (C-X-C motif) ligand 14 [Source:MGI Symbol;Acc:MGI:1888514]",
"c-mer proto-oncogene tyrosine kinase [Source:MGI Symbol;Acc:MGI:96965]",
"complement component 1, q subcomponent, beta polypeptide [Source:MGI Symbol;Acc:MGI:88224]",
"complement component 3 [Source:MGI Symbol;Acc:MGI:88227]", "early growth response 1 [Source:MGI Symbol;Acc:MGI:95295]",
"epidermal growth factor receptor pathway substrate 8 [Source:MGI Symbol;Acc:MGI:104684]",
"epidermal growth factor receptor [Source:MGI Symbol;Acc:MGI:95294]",
"eukaryotic translation initiation factor 2-alpha kinase 2 [Source:MGI Symbol;Acc:MGI:1353449]",
"guanylate binding protein 2 [Source:MGI Symbol;Acc:MGI:102772]",
"heme oxygenase (decycling) 1 [Source:MGI Symbol;Acc:MGI:96163]",
"high mobility group box 2 [Source:MGI Symbol;Acc:MGI:96157]",
"interferon, alpha-inducible protein 27 [Source:MGI Symbol;Acc:MGI:1277180]",
"interferon-induced protein with tetratricopeptide repeats 1 [Source:MGI Symbol;Acc:MGI:99450]",
"interferon-induced protein with tetratricopeptide repeats 3 [Source:MGI Symbol;Acc:MGI:1101055]",
"interferon induced transmembrane protein 3 [Source:MGI Symbol;Acc:MGI:1913391]",
"interferon induced with helicase C domain 1 [Source:MGI Symbol;Acc:MGI:1918836]",
"interferon regulatory factor 1 [Source:MGI Symbol;Acc:MGI:96590]",
"interferon regulatory factor 7 [Source:MGI Symbol;Acc:MGI:1859212]",
"interferon regulatory factor 9 [Source:MGI Symbol;Acc:MGI:107587]",
"ISG15 ubiquitin-like modifier [Source:MGI Symbol;Acc:MGI:1855694]",
"myxovirus (influenza virus) resistance 1 [Source:MGI Symbol;Acc:MGI:97243]",
"NLR family, CARD domain containing 5 [Source:MGI Symbol;Acc:MGI:3612191]",
"peroxisome proliferative activated receptor, gamma, coactivator 1 alpha [Source:MGI Symbol;Acc:MGI:1342774]",
"radical S-adenosyl methionine domain containing 2 [Source:MGI Symbol;Acc:MGI:1929628]",
"signal transducer and activator of transcription 1 [Source:MGI Symbol;Acc:MGI:103063]",
"suppressor of cytokine signaling 3 [Source:MGI Symbol;Acc:MGI:1201791]",
"TRAF-interacting protein with forkhead-associated domain [Source:MGI Symbol;Acc:MGI:2182965]",
"tripartite motif-containing 5 [Source:MGI Symbol;Acc:MGI:3646853]",
"tripartite motif-containing 67 [Source:MGI Symbol;Acc:MGI:3045323]",
"tumor necrosis factor receptor superfamily, member 1a [Source:MGI Symbol;Acc:MGI:1314884]",
"vascular endothelial growth factor A [Source:MGI Symbol;Acc:MGI:103178]",
"xanthine dehydrogenase [Source:MGI Symbol;Acc:MGI:98973]", "zinc finger CCCH type, antiviral 1 [Source:MGI Symbol;Acc:MGI:1926031]",
"zinc finger protein, multitype 2 [Source:MGI Symbol;Acc:MGI:1334444]"
), class = "factor"), gene_biotype = structure(c(1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L), .Label = "protein_coding", class = "factor")), row.names = c(NA,
-40L), .Names = c("ensembl_gene_id", "baseMean", "log2FoldChange",
"lfcSE", "stat", "pvalue", "padj", "gene_symbol", "entrezgene",
"description", "gene_biotype"), class = "data.frame")innateDB_bulk_up_genes <- innateDB_genes_NSCs_bulk_up_table %>% dplyr::arrange( desc( log2FoldChange )) %>% ungroup() %>% dplyr::select(ensembl_gene_id , gene_symbol )
innateDB_bulk_up_genes <- innateDB_bulk_up_genes[1:7,]We split the df into a list , rows become fields
genes_up_InnateDB_sms2_bulk_list <- split(innateDB_bulk_up_genes , f = seq(nrow(innateDB_bulk_up_genes)))Next up we calculate the totalReads and reads for only the specified gene, add the information for cells which age and celltype they belong to.
genes_up_InnateDB_data_list <- lapply(genes_up_InnateDB_sms2_bulk_list, function(x){
raw_count_data <- data.frame( totalReads = colSums(data_10X_NSCs[,as.character(cells_NSCs_10X)]) , geneReads = as.numeric(data_10X_NSCs[ as.character(x$ensembl_gene_id) ,as.character(cells_NSCs_10X)]) , gene = as.character(x$gene_symbol) ) %>% rownames_to_column("name") %>% left_join(anno_10X_NSCs , by = "name" )
table_cells_expr <- addmargins( table( raw_count_data$age , raw_count_data$geneReads > 0 ) )
# fig.width=6 , fig.height=2
gg <- ggplot(data = raw_count_data , mapping = aes(x = totalReads , y = geneReads ) ) + geom_point(aes(color = age)) + scale_color_manual(values = c("old" = "slateblue" , "young" = "yellowgreen") ) + ggtitle(x$gene_symbol)
list( "raw_count_data" = raw_count_data , "numbers_cells_expressing" = table_cells_expr , "plot" = gg )
})We extract the raw_counts data table from the list we have just created
genes_up_InnateDB_data_list_raw_counts <- lapply(genes_up_InnateDB_data_list, function(x){ x$raw_count_data })and bind together all the data frames for the different genes
genes_up_InnateDB_data_plot_10X <- bind_rows(genes_up_InnateDB_data_list_raw_counts)Scatterplot
gg_InnateDB_10X <- ggplot(data = genes_up_InnateDB_data_plot_10X , mapping = aes(x = totalReads , y = geneReads ) ) + geom_point(aes(color = age)) + scale_color_manual(values = c("old" = "slateblue" , "young" = "yellowgreen") ) + facet_grid( gene ~ . )gg_InnateDB_10X
Barplot
Show the same data as stacked barplots
genes_up_InnateDB_data_plot_10X_bars <- genes_up_InnateDB_data_plot_10X %>% mutate( interval = base::cut( geneReads , breaks = c(-0.1, 0.5,5.5,50.5,Inf) , labels = c("0","1-5","5-50","50-") ) ) %>% group_by(interval,gene,age) %>% summarise( counts = n() ) %>% ungroup() %>% mutate( interval = fct_rev( interval ) , age = fct_recode( .f = age , "22 month" = "old", "2 month" = "young" ) %>% fct_rev() ) %>% group_by(gene,age) %>% mutate( Percent = 100*counts/sum(counts) )ggplot( data = genes_up_InnateDB_data_plot_10X_bars , mapping = aes(x = age , y = Percent , fill = interval) ) + geom_bar(stat = "identity") + facet_grid(facets = . ~ gene ) + viridis::scale_fill_viridis( discrete = TRUE , direction = -1 , drop=FALSE , name = "Read count" ) + ggtitle("Read counts: 10X") + theme( axis.text.x = element_text(angle = 90,hjust = 0,vjust = 0.5) , strip.text.x = element_text(size = 8)) 
SMARTseq2
How many counts do genes have in the Single cell data sets, that are significantly upregulated in the bulk NSCs
cells_NSCs_SMARTseq2 <- as.character(anno_SMARTseq2_NSCs$name)Ifit1 - ENSMUSG00000078920
Ifit1_df_SMARTseq2 <- data.frame( totalReads = colSums(data_SMARTseq2_NSCs) , geneReads = as.numeric(data_SMARTseq2_NSCs["ENSMUSG00000078920",cells_NSCs_SMARTseq2]) , gene = "Ifit1" ) %>% rownames_to_column("name") %>% left_join(anno_SMARTseq2_NSCs , by = "name" )Column `name` joining character vector and factor, coercing into character vectoraddmargins( table( Ifit1_df_SMARTseq2$age , Ifit1_df_SMARTseq2$geneReads > 0 ) )
FALSE TRUE Sum
old 132 1 133
young 89 0 89
Sum 221 1 222gg_Ifit1_SMARTseq2 <- ggplot(data = Ifit1_df_SMARTseq2 , mapping = aes(x = totalReads , y = geneReads ) ) + geom_point(aes(color = age)) + scale_color_manual(values = c("old" = "slateblue" , "young" = "yellowgreen") )
gg_Ifit1_SMARTseq2
Rsad2 - ENSMUSG00000020641
Rsad2_df_SMARTseq2 <- data.frame( totalReads = colSums(data_SMARTseq2_NSCs) , geneReads = as.numeric(data_SMARTseq2_NSCs["ENSMUSG00000020641",cells_NSCs_SMARTseq2]) , gene = "Rsad2" ) %>% rownames_to_column("name") %>% left_join(anno_SMARTseq2_NSCs , by = "name" )Column `name` joining character vector and factor, coercing into character vectoraddmargins( table( Rsad2_df_SMARTseq2$age , Rsad2_df_SMARTseq2$geneReads > 0 ) )
FALSE TRUE Sum
old 122 11 133
young 86 3 89
Sum 208 14 222gg_Rsad2_SMARTseq2 <- ggplot(data = Rsad2_df_SMARTseq2 , mapping = aes(x = totalReads , y = geneReads ) ) + geom_point(aes(color = age)) + scale_color_manual(values = c("old" = "slateblue" , "young" = "yellowgreen") )
gg_Rsad2_SMARTseq2
C3 - ENSMUSG00000024164
C3_df_SMARTseq2 <- data.frame( totalReads = colSums(data_SMARTseq2_NSCs) , geneReads = as.numeric(data_SMARTseq2_NSCs["ENSMUSG00000024164",cells_NSCs_SMARTseq2]) , gene = "C3" ) %>% rownames_to_column("name") %>% left_join(anno_SMARTseq2_NSCs , by = "name" )Column `name` joining character vector and factor, coercing into character vectoraddmargins( table( C3_df_SMARTseq2$age , C3_df_SMARTseq2$geneReads > 0 ) )
FALSE TRUE Sum
old 133 0 133
young 88 1 89
Sum 221 1 222gg_C3_SMARTseq2 <- ggplot(data = C3_df_SMARTseq2 , mapping = aes(x = totalReads , y = geneReads ) ) + geom_point(aes(color = age)) + scale_color_manual(values = c("old" = "slateblue" , "young" = "yellowgreen") )
gg_C3_SMARTseq2
Iigp1 - ENSMUSG00000054072
Iigp1_df_SMARTseq2 <- data.frame( totalReads = colSums(data_SMARTseq2_NSCs) , geneReads = as.numeric(data_SMARTseq2_NSCs["ENSMUSG00000054072",cells_NSCs_SMARTseq2]) , gene = "Iigp1" ) %>% rownames_to_column("name") %>% left_join(anno_SMARTseq2_NSCs , by = "name" )Column `name` joining character vector and factor, coercing into character vectoraddmargins( table( Iigp1_df_SMARTseq2$age , Iigp1_df_SMARTseq2$geneReads > 0 ) )
FALSE TRUE Sum
old 116 17 133
young 89 0 89
Sum 205 17 222gg_Iigp1_SMARTseq2 <- ggplot(data = Iigp1_df_SMARTseq2 , mapping = aes(x = totalReads , y = geneReads ) ) + geom_point(aes(color = age)) + scale_color_manual(values = c("old" = "slateblue" , "young" = "yellowgreen") )
gg_Iigp1_SMARTseq2
Ifi44 - ENSMUSG00000028037
Ifi44_df_SMARTseq2 <- data.frame( totalReads = colSums(data_SMARTseq2_NSCs) , geneReads = as.numeric(data_SMARTseq2_NSCs["ENSMUSG00000028037",cells_NSCs_SMARTseq2]) , gene = "Ifi44" ) %>% rownames_to_column("name") %>% left_join(anno_SMARTseq2_NSCs , by = "name" )Column `name` joining character vector and factor, coercing into character vectoraddmargins( table( Ifi44_df_SMARTseq2$age , Ifi44_df_SMARTseq2$geneReads > 0 ) )
FALSE TRUE Sum
old 126 7 133
young 88 1 89
Sum 214 8 222gg_Ifi44_SMARTseq2 <- ggplot(data = Ifi44_df_SMARTseq2 , mapping = aes(x = totalReads , y = geneReads ) ) + geom_point(aes(color = age)) + scale_color_manual(values = c("old" = "slateblue" , "young" = "yellowgreen") )
gg_Ifi44_SMARTseq2
Cxcl10 - ENSMUSG00000034855
Cxcl10_df_SMARTseq2 <- data.frame( totalReads = colSums(data_SMARTseq2_NSCs) , geneReads = as.numeric(data_SMARTseq2_NSCs["ENSMUSG00000034855",cells_NSCs_SMARTseq2]) , gene = "Cxcl10" ) %>% rownames_to_column("name") %>% left_join(anno_SMARTseq2_NSCs , by = "name" )Column `name` joining character vector and factor, coercing into character vectoraddmargins( table( Cxcl10_df_SMARTseq2$age , Cxcl10_df_SMARTseq2$geneReads > 0 ) )
FALSE TRUE Sum
old 130 3 133
young 89 0 89
Sum 219 3 222gg_Cxcl10_SMARTseq2 <- ggplot(data = Cxcl10_df_SMARTseq2 , mapping = aes(x = totalReads , y = geneReads ) ) + geom_point(aes(color = age)) + scale_color_manual(values = c("old" = "slateblue" , "young" = "yellowgreen") )
gg_Cxcl10_SMARTseq2
Il33 - ENSMUSG00000024810
Il33_df_SMARTseq2 <- data.frame( totalReads = colSums(data_SMARTseq2_NSCs) , geneReads = as.numeric(data_SMARTseq2_NSCs["ENSMUSG00000024810",cells_NSCs_SMARTseq2]) , gene = "Il33" ) %>% rownames_to_column("name") %>% left_join(anno_SMARTseq2_NSCs , by = "name" )Column `name` joining character vector and factor, coercing into character vectoraddmargins( table( Il33_df_SMARTseq2$age , Il33_df_SMARTseq2$geneReads > 0 ) )
FALSE TRUE Sum
old 58 75 133
young 54 35 89
Sum 112 110 222gg_Il33_SMARTseq2 <- ggplot(data = Il33_df_SMARTseq2 , mapping = aes(x = totalReads , y = geneReads ) ) + geom_point(aes(color = age)) + scale_color_manual(values = c("old" = "slateblue" , "young" = "yellowgreen") )
gg_Il33_SMARTseq2
Ddx60 - ENSMUSG00000037921
Ddx60_df_SMARTseq2 <- data.frame( totalReads = colSums(data_SMARTseq2_NSCs) , geneReads = as.numeric(data_SMARTseq2_NSCs["ENSMUSG00000037921",cells_NSCs_SMARTseq2]) , gene = "Ddx60" ) %>% rownames_to_column("name") %>% left_join(anno_SMARTseq2_NSCs , by = "name" )Column `name` joining character vector and factor, coercing into character vectoraddmargins( table( Ddx60_df_SMARTseq2$age , Ddx60_df_SMARTseq2$geneReads > 0 ) )
FALSE TRUE Sum
old 113 20 133
young 87 2 89
Sum 200 22 222gg_Ddx60_SMARTseq2 <- ggplot(data = Ddx60_df_SMARTseq2 , mapping = aes(x = totalReads , y = geneReads ) ) + geom_point(aes(color = age)) + scale_color_manual(values = c("old" = "slateblue" , "young" = "yellowgreen") )
gg_Ddx60_SMARTseq2
Ifi47 - ENSMUSG00000078920
Ifi47_df_SMARTseq2 <- data.frame( totalReads = colSums(data_SMARTseq2_NSCs) , geneReads = as.numeric(data_SMARTseq2_NSCs["ENSMUSG00000078920",cells_NSCs_SMARTseq2]) , gene = "Ifi47" ) %>% rownames_to_column("name") %>% left_join(anno_SMARTseq2_NSCs , by = "name" )Column `name` joining character vector and factor, coercing into character vectoraddmargins( table( Ifi47_df_SMARTseq2$age , Ifi47_df_SMARTseq2$geneReads > 0 ) )
FALSE TRUE Sum
old 132 1 133
young 89 0 89
Sum 221 1 222gg_Ifi47_SMARTseq2 <- ggplot(data = Ifi47_df_SMARTseq2 , mapping = aes(x = totalReads , y = geneReads ) ) + geom_point(aes(color = age)) + scale_color_manual(values = c("old" = "slateblue" , "young" = "yellowgreen") )
gg_Ifi47_SMARTseq2
Gm4951 - ENSMUSG00000073555
Gm4951_df_SMARTseq2 <- data.frame( totalReads = colSums(data_SMARTseq2_NSCs) , geneReads = as.numeric(data_SMARTseq2_NSCs["ENSMUSG00000073555",cells_NSCs_SMARTseq2]) , gene = "Gm4951" ) %>% rownames_to_column("name") %>% left_join(anno_SMARTseq2_NSCs , by = "name" )Column `name` joining character vector and factor, coercing into character vectoraddmargins( table( Gm4951_df_SMARTseq2$age , Gm4951_df_SMARTseq2$geneReads > 0 ) )
FALSE TRUE Sum
old 120 13 133
young 89 0 89
Sum 209 13 222gg_Gm4951_SMARTseq2 <- ggplot(data = Gm4951_df_SMARTseq2 , mapping = aes(x = totalReads , y = geneReads ) ) + geom_point(aes(color = age)) + scale_color_manual(values = c("old" = "slateblue" , "young" = "yellowgreen") )
gg_Gm4951_SMARTseq2
Slc1a3 - ENSMUSG00000005360
Slc1a3_df_SMARTseq2 <- data.frame( totalReads = colSums(data_SMARTseq2_NSCs) , geneReads = as.numeric(data_SMARTseq2_NSCs["ENSMUSG00000005360",cells_NSCs_SMARTseq2]) , gene = "Slc1a3" ) %>% rownames_to_column("name") %>% left_join(anno_SMARTseq2_NSCs , by = "name" )Column `name` joining character vector and factor, coercing into character vectoraddmargins( table( Slc1a3_df_SMARTseq2$age , Slc1a3_df_SMARTseq2$geneReads > 0 ) )
TRUE Sum
old 133 133
young 89 89
Sum 222 222gg_Slc1a3_SMARTseq2 <- ggplot(data = Slc1a3_df_SMARTseq2 , mapping = aes(x = totalReads , y = geneReads ) ) + geom_point(aes(color = age)) + scale_color_manual(values = c("old" = "slateblue" , "young" = "yellowgreen") )
gg_Slc1a3_SMARTseq2
Sox2 - ENSMUSG00000074637
Sox2_df_SMARTseq2 <- data.frame( totalReads = colSums(data_SMARTseq2_NSCs) , geneReads = as.numeric(data_SMARTseq2_NSCs["ENSMUSG00000074637",cells_NSCs_SMARTseq2]) , gene = "Sox2" ) %>% rownames_to_column("name") %>% left_join(anno_SMARTseq2_NSCs , by = "name" )Column `name` joining character vector and factor, coercing into character vectoraddmargins( table( Sox2_df_SMARTseq2$age , Sox2_df_SMARTseq2$geneReads > 0 ) )
FALSE TRUE Sum
old 48 85 133
young 25 64 89
Sum 73 149 222gg_Sox2_SMARTseq2 <- ggplot(data = Sox2_df_SMARTseq2 , mapping = aes(x = totalReads , y = geneReads ) ) + geom_point(aes(color = age)) + scale_color_manual(values = c("old" = "slateblue" , "young" = "yellowgreen") )
gg_Sox2_SMARTseq2
Combine all genes
interferon_genes_bulk_df_SMARTseq2 <- bind_rows( Ifit1_df_SMARTseq2 , C3_df_SMARTseq2 , Rsad2_df_SMARTseq2 , Iigp1_df_SMARTseq2 , Ifi47_df_SMARTseq2 , Il33_df_SMARTseq2 , Cxcl10_df_SMARTseq2 , Sox2_df_SMARTseq2 , Slc1a3_df_SMARTseq2 )Unequal factor levels: coercing to characterbinding character and factor vector, coercing into character vectorbinding character and factor vector, coercing into character vectorbinding character and factor vector, coercing into character vectorbinding character and factor vector, coercing into character vectorbinding character and factor vector, coercing into character vectorbinding character and factor vector, coercing into character vectorbinding character and factor vector, coercing into character vectorbinding character and factor vector, coercing into character vectorbinding character and factor vector, coercing into character vectorScatterplot
gg_interferon_genes_SMARTseq2 <- ggplot(data = interferon_genes_bulk_df_SMARTseq2 , mapping = aes(x = totalReads , y = geneReads ) ) + geom_point(aes(color = age)) + scale_color_manual(values = c("old" = "slateblue" , "young" = "yellowgreen") ) + facet_grid( gene ~ . ) + scale_y_log10() + ylab("log10( geneReads )")gg_interferon_genes_SMARTseq2
Barplot
Show the same data as stacked barplots
interferon_genes_bulk_df_SMARTseq2_bars <- interferon_genes_bulk_df_SMARTseq2 %>% mutate( interval = base::cut( geneReads , breaks = c(-0.1, 0.5,5.5,50.5,Inf) , labels = c("0","1-5","5-50","50-") ) ) %>% group_by(interval,gene,age) %>% summarise( counts = n() ) %>% ungroup() %>% mutate( interval = fct_rev( interval ) , age = fct_recode( .f = age , "22 month" = "old", "2 month" = "young" ) %>% fct_rev() ) %>% group_by(gene,age) %>% mutate( Percent = 100*counts/sum(counts) )ggplot( data = interferon_genes_bulk_df_SMARTseq2_bars , mapping = aes(x = age , y = Percent , fill = interval) ) + geom_bar(stat = "identity") + facet_grid(facets = . ~ gene ) + viridis::scale_fill_viridis( discrete = TRUE , direction = -1 ) + ggtitle("Read counts: SMARTseq2")
Bulk NSC genes in InnateDB
Which genes are the ones with high fold changes in the bulk and are annotated in InnateDB
innateDB_bulk_up_genes <- innateDB_genes_NSCs_bulk_up_table %>% dplyr::arrange( desc( log2FoldChange )) %>% ungroup() %>% dplyr::select(ensembl_gene_id , gene_symbol )
innateDB_bulk_up_genes <- innateDB_bulk_up_genes[1:7,]We split the df into a list , rows become fields
genes_up_InnateDB_sms2_bulk_list <- split(innateDB_bulk_up_genes , f = seq(nrow(innateDB_bulk_up_genes)))Next up we calculate the totalReads and reads for only the specified gene, add the information for cells which age and celltype they belong to.
genes_up_InnateDB_data_list_SMARTseq2 <- lapply(genes_up_InnateDB_sms2_bulk_list, function(x){
raw_count_data <- data.frame( totalReads = colSums(data_SMARTseq2_NSCs[,as.character(cells_NSCs_SMARTseq2)]) , geneReads = as.numeric(data_SMARTseq2_NSCs[ as.character(x$ensembl_gene_id) ,as.character(cells_NSCs_SMARTseq2) ]) , gene = as.character( x$gene_symbol ) ) %>% rownames_to_column("name") %>% left_join(anno_SMARTseq2_NSCs , by = "name" )
table_cells_expr <- addmargins( table( raw_count_data$age , raw_count_data$geneReads > 0 ) )
# fig.width=6 , fig.height=2
gg <- ggplot(data = raw_count_data , mapping = aes(x = totalReads , y = geneReads ) ) + geom_point(aes(color = age)) + scale_color_manual(values = c("old" = "slateblue" , "young" = "yellowgreen") ) + ggtitle(x$gene_symbol)
list( "raw_count_data" = raw_count_data , "numbers_cells_expressing" = table_cells_expr , "plot" = gg )
})We extract the raw_counts data table from the list we have just created
genes_up_InnateDB_data_list_SMARTseq2_raw_counts <- lapply(genes_up_InnateDB_data_list_SMARTseq2, function(x){ x$raw_count_data })and bind together all the data frames for the different genes
genes_up_InnateDB_data_plot_SMARTseq2 <- bind_rows(genes_up_InnateDB_data_list_SMARTseq2_raw_counts)Scatterplot
gg_InnateDB_SMARTseq2 <- ggplot(data = genes_up_InnateDB_data_plot_SMARTseq2 , mapping = aes(x = totalReads , y = geneReads ) ) + geom_point(aes(color = age)) + scale_color_manual(values = c("old" = "slateblue" , "young" = "yellowgreen") ) + facet_grid( gene ~ . )gg_InnateDB_SMARTseq2
Barplot
Show the same data as stacked barplots
genes_up_InnateDB_data_plot_SMARTseq2_bars <- genes_up_InnateDB_data_plot_SMARTseq2 %>% mutate( interval = base::cut( geneReads , breaks = c(-0.1, 0.5,5.5,50.5,Inf) , labels = c("0","1-5","5-50","50-") ) ) %>% group_by(interval,gene,age) %>% summarise( counts = n() ) %>% ungroup() %>% mutate( interval = fct_rev( interval ) , age = fct_recode( .f = age , "22 month" = "old", "2 month" = "young" ) %>% fct_rev() ) %>% group_by(gene,age) %>% mutate( Percent = 100*counts/sum(counts) )ggplot( data = genes_up_InnateDB_data_plot_SMARTseq2_bars , mapping = aes(x = age , y = Percent , fill = interval) ) + geom_bar(stat = "identity") + facet_grid(facets = . ~ gene ) + viridis::scale_fill_viridis( discrete = TRUE , drop=FALSE , direction = -1 , name = "Read count" ) + ggtitle("Read counts: SMARTseq2") + theme( axis.text.x = element_text(angle = 90,hjust = 0,vjust = 0.5))
Combined Barplots for 10X and SMARTseq2
genes_up_InnateDB_data_barplot <- bind_rows(
dplyr::mutate( genes_up_InnateDB_data_plot_SMARTseq2_bars , seqmethod = "Smart-Seq2" ) ,
dplyr::mutate( genes_up_InnateDB_data_plot_10X_bars , seqmethod = "10X" )
)idx = 1
gg1 <- ggplot( data = genes_up_InnateDB_data_barplot %>% dplyr::filter( gene == innateDB_bulk_up_genes$gene_symbol[[idx]] ) , mapping = aes(x = age , y = Percent , fill = interval) ) + geom_bar(stat = "identity") + facet_grid(facets = . ~ seqmethod ) + viridis::scale_fill_viridis( discrete = TRUE , drop=FALSE , direction = -1 , name = "Read count" ) + ggtitle( paste("Read counts:", innateDB_bulk_up_genes$gene_symbol[[idx]] ) ) + theme( axis.text.x = element_text(angle = 90,hjust = 0,vjust = 0.5))
gg1
idx = 2
gg2 <- ggplot( data = genes_up_InnateDB_data_barplot %>% dplyr::filter( gene == innateDB_bulk_up_genes$gene_symbol[idx] ) , mapping = aes(x = age , y = Percent , fill = interval) ) + geom_bar(stat = "identity") + facet_grid(facets = . ~ seqmethod ) + viridis::scale_fill_viridis( discrete = TRUE , drop=FALSE , direction = -1 , name = "Read count" ) + ggtitle( paste("Read counts:", innateDB_bulk_up_genes$gene_symbol[[idx]] ) ) + theme( axis.text.x = element_text(angle = 90,hjust = 0,vjust = 0.5))
gg2
idx = 3
gg3 <- ggplot( data = genes_up_InnateDB_data_barplot %>% dplyr::filter( gene == innateDB_bulk_up_genes$gene_symbol[idx] ) , mapping = aes(x = age , y = Percent , fill = interval) ) + geom_bar(stat = "identity") + facet_grid(facets = . ~ seqmethod ) + viridis::scale_fill_viridis( discrete = TRUE , drop=FALSE , direction = -1 , name = "Read count" ) + ggtitle( paste("Read counts:", innateDB_bulk_up_genes$gene_symbol[[idx]] ) ) + theme( axis.text.x = element_text(angle = 90,hjust = 0,vjust = 0.5))
gg3
idx = 4
gg4 <- ggplot( data = genes_up_InnateDB_data_barplot %>% dplyr::filter( gene == innateDB_bulk_up_genes$gene_symbol[idx] ) , mapping = aes(x = age , y = Percent , fill = interval) ) + geom_bar(stat = "identity") + facet_grid(facets = . ~ seqmethod ) + viridis::scale_fill_viridis( discrete = TRUE , drop=FALSE , direction = -1 , name = "Read count" ) + ggtitle( paste("Read counts:", innateDB_bulk_up_genes$gene_symbol[[idx]] ) ) + theme( axis.text.x = element_text(angle = 90,hjust = 0,vjust = 0.5))
gg4
idx = 5
gg5 <- ggplot( data = genes_up_InnateDB_data_barplot %>% dplyr::filter( gene == innateDB_bulk_up_genes$gene_symbol[idx] ) , mapping = aes(x = age , y = Percent , fill = interval) ) + geom_bar(stat = "identity") + facet_grid(facets = . ~ seqmethod ) + viridis::scale_fill_viridis( discrete = TRUE , drop=FALSE , direction = -1 , name = "Read count" ) + ggtitle( paste("Read counts:", innateDB_bulk_up_genes$gene_symbol[[idx]] ) ) + theme( axis.text.x = element_text(angle = 90,hjust = 0,vjust = 0.5))
gg5
idx = 6
gg6 <- ggplot( data = genes_up_InnateDB_data_barplot %>% dplyr::filter( gene == innateDB_bulk_up_genes$gene_symbol[idx] ) , mapping = aes(x = age , y = Percent , fill = interval) ) + geom_bar(stat = "identity") + facet_grid(facets = . ~ seqmethod ) + viridis::scale_fill_viridis( discrete = TRUE , drop=FALSE , direction = -1 , name = "Read count" ) + ggtitle( paste("Read counts:", innateDB_bulk_up_genes$gene_symbol[[idx]] ) ) + theme( axis.text.x = element_text(angle = 90,hjust = 0,vjust = 0.5))
gg6
idx = 7
gg7 <- ggplot( data = genes_up_InnateDB_data_barplot %>% dplyr::filter( gene == innateDB_bulk_up_genes$gene_symbol[idx] ) , mapping = aes(x = age , y = Percent , fill = interval) ) + geom_bar(stat = "identity") + facet_grid(facets = . ~ seqmethod ) + viridis::scale_fill_viridis( discrete = TRUE , drop=FALSE , direction = -1 , name = "Read count" ) + ggtitle( paste("Read counts:", innateDB_bulk_up_genes$gene_symbol[[idx]] ) ) + theme( axis.text.x = element_text(angle = 90,hjust = 0,vjust = 0.5))
gg7
gridExtra::grid.arrange(gg1, gg2, gg3 , gg4, gg5, gg6 , gg7 , nrow = 1)
SessionInfo
sessionInfo()R version 3.4.3 (2017-11-30)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 16.04.3 LTS
Matrix products: default
BLAS: /usr/lib/libblas/libblas.so.3.6.0
LAPACK: /usr/lib/lapack/liblapack.so.3.6.0
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8 LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] parallel stats graphics grDevices utils datasets methods base
other attached packages:
[1] biomaRt_2.34.2 ggbeeswarm_0.6.0 viridisLite_0.3.0 bindrcpp_0.2 forcats_0.3.0 dplyr_0.7.4
[7] ggrepel_0.8.0 Seurat_2.2.0 Biobase_2.38.0 BiocGenerics_0.24.0 Matrix_1.2-14 cowplot_0.7.0
[13] ggplot2_2.2.1 pheatmap_1.0.10 tibble_1.4.2 stringr_1.3.1 tidyr_0.7.2
loaded via a namespace (and not attached):
[1] readxl_1.1.0 backports_1.1.2 Hmisc_4.1-1 VGAM_1.0-3 NMF_0.20.6
[6] sn_1.5-0 plyr_1.8.4 igraph_1.0.1 lazyeval_0.2.0 splines_3.4.3
[11] gridBase_0.4-7 digest_0.6.12 foreach_1.4.3 htmltools_0.3.6 viridis_0.4.1
[16] lars_1.2 gdata_2.17.0 memoise_1.0.0 magrittr_1.5 checkmate_1.8.5
[21] cluster_2.0.6 doParallel_1.0.10 mixtools_1.0.4 ROCR_1.0-7 openxlsx_4.1.0
[26] R.utils_2.6.0 prettyunits_1.0.2 colorspace_1.3-2 blob_1.1.1 haven_1.1.2
[31] RCurl_1.95-4.10 crayon_1.3.4 jsonlite_1.5 lme4_1.1-18-1 bindr_0.1
[36] survival_2.41-3 iterators_1.0.8 ape_5.1 glue_1.3.0 registry_0.3
[41] gtable_0.2.0 MatrixModels_0.4-1 car_3.0-2 kernlab_0.9-25 prabclus_2.2-6
[46] DEoptimR_1.0-8 abind_1.4-5 scales_0.5.0 mvtnorm_1.0-5 DBI_1.0.0
[51] rngtools_1.2.4 Rcpp_0.12.18 dtw_1.20-1 progress_1.2.0 xtable_1.8-2
[56] htmlTable_1.12 tclust_1.2-3 bit_1.1-14 foreign_0.8-69 proxy_0.4-22
[61] mclust_5.2.2 SDMTools_1.1-221 Formula_1.2-3 tsne_0.1-3 stats4_3.4.3
[66] httr_1.3.1 htmlwidgets_1.2 FNN_1.1 gplots_3.0.1 RColorBrewer_1.1-2
[71] fpc_2.1-10 acepack_1.4.1 modeltools_0.2-22 ica_1.0-2 XML_3.98-1.11
[76] pkgconfig_2.0.2 R.methodsS3_1.7.1 flexmix_2.3-13 nnet_7.3-12 caret_6.0-73
[81] AnnotationDbi_1.40.0 labeling_0.3 rlang_0.2.2 reshape2_1.4.2 munsell_0.4.3
[86] cellranger_1.1.0 tools_3.4.3 RSQLite_2.1.1 ranger_0.6.0 ggridges_0.5.0
[91] evaluate_0.11 yaml_2.2.0 bit64_0.9-7 ModelMetrics_1.1.0 knitr_1.20
[96] zip_1.0.0 robustbase_0.92-7 caTools_1.17.1 purrr_0.2.4 pbapply_1.3-1
[101] nlme_3.1-131 R.oo_1.22.0 compiler_3.4.3 rstudioapi_0.7 beeswarm_0.2.3
[106] curl_3.2 stringi_1.2.4 lattice_0.20-35 trimcluster_0.1-2 nloptr_1.0.4
[111] diffusionMap_1.1-0.1 pillar_1.3.0 data.table_1.11.6 bitops_1.0-6 irlba_2.1.2
[116] R6_2.2.2 latticeExtra_0.6-28 KernSmooth_2.23-15 gridExtra_2.2.1 rio_0.5.10
[121] IRanges_2.12.0 vipor_0.4.5 codetools_0.2-15 boot_1.3-20 MASS_7.3-48
[126] gtools_3.5.0 assertthat_0.2.0 pkgmaker_0.22 rprojroot_1.3-2 mnormt_1.5-5
[131] S4Vectors_0.16.0 diptest_0.75-7 hms_0.4.2 grid_3.4.3 rpart_4.1-12
[136] minqa_1.2.4 class_7.3-14 rmarkdown_1.10 segmented_0.5-1.4 carData_3.0-1
[141] Rtsne_0.11 numDeriv_2016.8-1 scatterplot3d_0.3-41 base64enc_0.1-3 ---
title: "Comparison of bulk sequencing, SMARTseq2 data and 10X single cell data"
output: 
  html_notebook: 
    toc: yes
    toc_float: true
    toc_collapsed: true
  toc_depth: 3
---

# Load the packages used

```{r}
library("tidyr")
library("stringr")
library("tibble")
library("pheatmap")
library("Seurat")
library("ggrepel")
library("dplyr")
library("forcats")
```

# Load all the data

We have three different sequencing experiments and want to cross compare between

- Bulk sequencing of NSCs, Neuroblasts, Microglia and Endothelial Cells
- Single Cell RNA sequencing of Neural Stem cells with
  - SMARTseq2 protocol: qNSC1 - aNSC2
  - 10X protocol: qNSC1 - NB + OPC & OD


Define colors for young and old throughout the analysis

```{r}
age_colors <- c(young = "yellowgreen" , old = "slateblue")
age_colors_bulk <- c(young = "yellowgreen" , old = "slateblue" , Mo7 = "slateblue1")
```


## Load the bulk sequencing TPM values

```{r}
bulk_data <- read.csv(file = "../Bulk_sequencing/count_table/Gene_expression_matrix_population.csv" , row.names = 1)

bulk_data <- as.matrix(bulk_data)
```

Log2 the TPM values

```{r}
bulk_data <- log2(bulk_data+1)
```


Also we load the annotation

```{r}
bulk_annotation <- read.csv(file = "../Bulk_sequencing/annotation/annotation_bulk.csv" , row.names = 1)
```

```{r}
bulk_annotation$type <- factor(x = bulk_annotation$type , levels = c("NSC","Neuroblast","Microglia","Endothelial"))
bulk_annotation$age <- factor(x = bulk_annotation$age , levels = c("young","Mo7","old"))
```


Which celltypes do we have?

```{r}
bulk_celltypes <- unique(as.character(bulk_annotation$type))

bulk_celltypes
```



## Load the SMARTseq2 sequencing TPM values

```{r}
smartseq2_data <- read.csv(file = "../SmartSeq2/count_table/TPM_NSCs.csv") %>% dplyr::select(-X) %>% column_to_rownames(var = "ensembl_gene_id")

smartseq2_data <- as.matrix(smartseq2_data)
```

Log2 the TPM values

```{r}
smartseq2_data <- log2(smartseq2_data+1)
```


Also we load the annotation

```{r}
smartseq2_annotation <- read.csv(file = "../SmartSeq2/count_table/NSCs_annotation.csv" , row.names = 1)
```

```{r}
smartseq2_annotation$type <- fct_recode(.f = smartseq2_annotation$type , qNSC1 = "q1"  , qNSC2 = "q2" , aNSC1 = "a1" , aNSC2 = "a2")
smartseq2_annotation$type <- factor(x = smartseq2_annotation$type , levels = c("qNSC1","qNSC2","aNSC1","aNSC2"))
smartseq2_annotation$age <- factor(x = smartseq2_annotation$age , levels = c("young","old"))
```

Which celltypes do we have?

```{r}
smartseq2_celltype_colors <- c( qNSC1 = "steelblue" , qNSC2 = "steelblue1" , aNSC1 = "tomato" , aNSC2 = "sienna1")
```

## Load the 10X sequencing normalized values

```{r}
seurat_10X2 <- readRDS(file = "../10X/seurat_10X2_clustered_min_1500_nGene.RDS" )
```

We can extract the normalized expression from the object

```{r}
tenx_data <- expm1( seurat_10X2@data )

tenx_data <- as.matrix(tenx_data)
```

Log2 the TPM values

```{r}
tenx_data <- log2(tenx_data+1)
```

Also we extract the annotation from the object

```{r}
tenx_annotation <- FetchData(object = seurat_10X2 , vars.all = c("celltype","age") ) %>% rownames_to_column("cell") %>% dplyr::rename(type = celltype)
```

```{r}
tenx_annotation$type <- factor(x = tenx_annotation$type , levels = c("qNSC1","qNSC2","aNSC0","aNSC1","aNSC2","TAP","NB","OPC","OD"))
tenx_annotation$age <- factor(x = tenx_annotation$age , levels = c("young","old"))
```


Which celltypes do we have?

```{r}
tenx_celltype_colors <- c( qNSC1 = "steelblue" , qNSC2 = "steelblue1" , aNSC0 = "tomato" , aNSC1 = "sienna1", aNSC2 = "sienna3" , TAP = "green" , NB = "yellow" , OPC = "pink" , OD = "violet")
```

Also we can load the genes.tsv file from the CellRanger Output which contains a mapping of ENSEMBL Gene IDs to the Gene names used in the 10X object

```{r}
tenx_gene_2_ensembl_tbl <- read.table(file = "../10X/count_table/filtered_gene_bc_matrices_mex/mm10/genes.tsv") %>% dplyr::rename( ensembl_gene_id = V1 , gene_symbol = V2 )
```

We can translate all the gene symbols to ENSEMBL Gene IDS

```{r}
tenx_ensembl_gene_id <- unlist(lapply( X = rownames(tenx_data) , FUN = function(gene){
    as.character( tenx_gene_2_ensembl_tbl[ which(tenx_gene_2_ensembl_tbl$gene_symbol == gene) , "ensembl_gene_id" ] )[1] 
  } ))
```

Add the ENSEMBL Gene IDs as rownames

```{r}
tenx_data_ensembl <- tenx_data

rownames(tenx_data_ensembl) <- tenx_ensembl_gene_id
```

## Load differentially expressed gene lists

### Bulk sequencing

#### NSCs

```{r}
bulk_DE_genes_NSC <- read.csv(file = "../Bulk_sequencing/DESeq2_results_old_vs_young/GP_DESeq2_results_Old_vs_Yang.csv" , row.names = 1) %>% mutate(gene_symbol = str_to_title(gene_symbol) )
```

```{r}
bulk_DE_genes_NSC_Mo7_vs_young <- read.csv(file = "../Bulk_sequencing/DESeq2_results_old_vs_young/GP_DESeq2_results_M7_vs_Young.csv" , row.names = 1)  %>% mutate(gene_symbol = str_to_title(gene_symbol) )
```


#### Neuroblasts

```{r}
bulk_DE_genes_NB <- read.csv(file = "../Bulk_sequencing/DESeq2_results_old_vs_young/PSA_DESeq2_results_Old_vs_Yang.csv" , row.names = 1)  %>% mutate(gene_symbol = str_to_title(gene_symbol) )
```

#### Microglia

```{r}
bulk_DE_genes_Microglia <- read.csv(file = "../Bulk_sequencing/DESeq2_results_old_vs_young/CD11b_DESeq2_results_Old_vs_Yang.csv" , row.names = 1)  %>% mutate(gene_symbol = str_to_title(gene_symbol) )
```

#### Endothelial Cells

```{r}
bulk_DE_genes_Endothelial <- read.csv(file = "../Bulk_sequencing/DESeq2_results_old_vs_young/CD31_DESeq2_results_Old_vs_Yang.csv" , row.names = 1)  %>% mutate(gene_symbol = str_to_title(gene_symbol) )
```

Make a list of the differentially expressed genes

```{r}
bulk_DE_genes <- list( NSC = bulk_DE_genes_NSC , NB = bulk_DE_genes_NB , Microglia = bulk_DE_genes_Microglia , Endothelial = bulk_DE_genes_Endothelial )
```

## MA Plots  

```{r}
MAPlot <- function( x , main = "" , highlight_genes = c() , data = NULL , SMARTseq2 = FALSE , labeL_top_genes = FALSE , max_foldChange = 10 , min_baseMean = 0.1  ,  max_baseMean = 1000000 ){
  require(dplyr)
  require(ggplot2)
  require(ggrepel)
  
  if(!is.null(data)){
    rowmean <- rowMeans(data)
    
    rowmean_df <- data.frame(baseMean = as.numeric(rowmean) , gene_symbol = as.character( names(rowmean) ) )
    
    if(SMARTseq2){
      colnames(rowmean_df)[2] <- "ensembl_gene_id"
      
      x <- left_join(x = x, y = rowmean_df , by = "ensembl_gene_id" )
    }else{
      x <- left_join(x = x, y = rowmean_df , by = "gene_symbol" )
    }
  }
  
  x_sig <- filter( x , padj < 0.05 & abs(log2FoldChange) > 1 )
  
  if( labeL_top_genes ){ x_label <- filter( x_sig ,  abs(log2FoldChange) > 4 ) }
  
  x_highlight <- filter( x , gene_symbol %in% highlight_genes )
  
  g <- ggplot(data = x , mapping = aes(x = baseMean , y = log2FoldChange )) + geom_point(color = "grey" ) + geom_point(data = x_sig , color = "red" ) + geom_point(data = x_highlight , color = "black", fill = "cyan"  , size = 2 , pch = 21) 
  
  if( labeL_top_genes ){
    g <- g + geom_text_repel(data = x_label , mapping = aes( x = baseMean , y = log2FoldChange , label = gene_symbol))
  }
  
  g <- g + scale_x_log10(limits = c(min_baseMean, max_baseMean ), expand = c(0, 0)) + ggtitle(main) + ylim(c(-max_foldChange,max_foldChange))
  
  g
}

```


### Bulk {.tabset .tabset-fade .tabset-pills}

#### NSC {.tabset .tabset-fade .tabset-pills}

##### With Labels

```{r fig.width=15}
MAPlot(x = bulk_DE_genes_NSC , main = "NSC (19 Month vs 2 Month)" , labeL_top_genes = TRUE )
```



```{r fig.width=15}
MAPlot(x = bulk_DE_genes_NSC_Mo7_vs_young, main = "NSC (7 Month vs 2 Month)" , labeL_top_genes = TRUE)
```

##### Without Labels

```{r fig.width=15}
MAPlot(x = bulk_DE_genes_NSC , main = "NSC (19 Month vs 2 Month)" )
```

```{r fig.width=15}
MAPlot(x = bulk_DE_genes_NSC_Mo7_vs_young , main = "NSC (7 Month vs 2 Month)")
```

#### NB

```{r fig.width=15}
MAPlot(x = bulk_DE_genes_NB , main = "NB")
```

```{r fig.width=15}
MAPlot(x = bulk_DE_genes_NB , main = "NB" , labeL_top_genes = TRUE)
```


#### Microglia

```{r fig.width=15}
MAPlot(x = bulk_DE_genes_Microglia , main = "Microglia")
```

```{r fig.width=15}
MAPlot(x = bulk_DE_genes_Microglia , main = "Microglia" , labeL_top_genes = TRUE)
```

#### Endothelial cells

```{r fig.width=15}
MAPlot(x = bulk_DE_genes_Endothelial , main = "Endothelial")
```

```{r fig.width=15}
MAPlot(x = bulk_DE_genes_Endothelial , main = "Endothelial" , labeL_top_genes = TRUE)
```


## Heatmaps of gene expression

```{r}
HeatmapGenes <- function( x , genes = c() , topVar = NULL , main = "" , annotation , annotation_colors , cluster_cols = FALSE , cluster_rows = TRUE , show_rownames = FALSE , show_colnames = FALSE , arrange_by_age = FALSE , keep_non_expressed = FALSE ){
  require("pheatmap")
  require("viridisLite")
  
  # The cells in the annotation are taken as the cells to be plotted
  annotation <- remove_rownames(df = annotation)
  
  if(! arrange_by_age){
    annotation <- annotation %>% arrange( type , age)
  }else{
    annotation <- annotation %>% arrange( age , type)
  }
  
  if("cell" %in% colnames(annotation)){ names_cols <- annotation %>% pull("cell") %>% as.character() }
  if("sample" %in% colnames(annotation)){ names_cols <- annotation %>% pull("sample") %>% as.character() }
  
  # Then the annotation data.frame is prepared as needed for the pheatmap function with the cell names as rownames
  if("cell" %in% colnames(annotation)){annotation <- column_to_rownames(df = annotation , var = "cell")}
  if("sample" %in% colnames(annotation)){annotation <- column_to_rownames(df = annotation , var = "sample")}
  # We set the order of columns of the annotation to be first age then type 
  annotation <- annotation[,c("age","type")]
  
  # We check for the genes from the list that are available in our data
  genes <- as.character(genes)
  genes_avail <- genes[genes %in% rownames(x)]
  
  if(!is.null(topVar)){
    x <- x[,names_cols]
    
    var_genes <- names(sort(apply(X = x, MARGIN = 1, FUN = var), decreasing = TRUE)[1:topVar])
    var_genes <- var_genes[!is.na(var_genes)]
    
    x_mat <- x[var_genes ,] 
  }else{
    # Only take the values for genes that are from the gene list
    x_mat <- x[genes_avail,names_cols]
  }

  # Dicard all the non expressed genes as they would create problems during the clustering process
  if(!keep_non_expressed){
    x <- x[apply(X = x, MARGIN = 1, FUN = var) > 0,]
  }
  if(keep_non_expressed & cluster_rows){warning("Row clustering cannot be used if unexpressed genes are kept!")}
  
  pheatmap(
    mat = x_mat ,
    cluster_rows = cluster_rows , 
    cluster_cols = cluster_cols ,
    color = viridis(n = 100),
    show_rownames = show_rownames,
    show_colnames = show_colnames ,
    clustering_method = "complete", 
    clustering_distance_rows = "euclidean" ,
    border_color = NA,
    scale = "none",
    annotation_col = annotation,
    main = main,
    fontsize = 5,
    annotation_colors = annotation_colors
)
}
```

### SMARTseq2 data {.tabset .tabset-fade .tabset-pills}

#### Top Variable Genes in subpopulations {.tabset .tabset-fade .tabset-pills}

##### qNSC1: Top 5000 variable genes 

```{r}
HeatmapGenes(x = smartseq2_data , topVar = 5000 , main = "SMARTseq2: qNSC1 young and old Top variable genes" , annotation = filter(smartseq2_annotation , type %in% c("qNSC1")) , annotation_colors = list(age = age_colors , type = smartseq2_celltype_colors ) , cluster_cols = TRUE , cluster_rows = TRUE)
```

##### qNSC2: Top 5000 variable genes 

```{r}
HeatmapGenes(x = smartseq2_data , topVar = 5000 , main = "SMARTseq2: qNSC2 young and old Top variable genes" , annotation = filter(smartseq2_annotation , type %in% c("qNSC2")) , annotation_colors = list(age = age_colors , type = smartseq2_celltype_colors ) , cluster_cols = TRUE , cluster_rows = TRUE)
```

##### aNSC1: Top 5000 variable genes 

```{r}
HeatmapGenes(x = smartseq2_data , topVar = 5000 , main = "SMARTseq2: aNSC1 young and old Top variable genes" , annotation = filter(smartseq2_annotation , type %in% c("aNSC1")) , annotation_colors = list(age = age_colors , type = smartseq2_celltype_colors ) , cluster_cols = TRUE , cluster_rows = TRUE)
```

##### aNSC2: Top 5000 variable genes

```{r}
HeatmapGenes(x = smartseq2_data , topVar = 5000 , main = "SMARTseq2: aNSC2 young and old Top variable genes" , annotation = filter(smartseq2_annotation , type %in% c("aNSC2")) , annotation_colors = list(age = age_colors , type = smartseq2_celltype_colors ) , cluster_cols = TRUE , cluster_rows = TRUE)
```

### 10X data {.tabset .tabset-fade .tabset-pills}

#### Top Variable Genes in subpopulations {.tabset .tabset-fade .tabset-pills}

##### qNSC1: Top 5000 variable genes 

```{r}
# HeatmapGenes(x = tenx_data_ensembl , topVar = 5000 , main = "10X: qNSC1 young and old Top variable genes" , annotation = filter(tenx_annotation , type %in% c("qNSC1")) , annotation_colors = list(age = age_colors , type = tenx_celltype_colors ) , cluster_cols = TRUE , cluster_rows = TRUE)
```

##### qNSC2: Top 5000 variable genes 

```{r}
# HeatmapGenes(x = tenx_data_ensembl , topVar = 5000 , main = "10X: qNSC2 young and old Top variable genes" , annotation = filter(tenx_annotation , type %in% c("qNSC2")) , annotation_colors = list(age = age_colors , type = tenx_celltype_colors ) , cluster_cols = TRUE , cluster_rows = TRUE )
```

##### aNSC0: Top 5000 variable genes 

```{r}
# HeatmapGenes(x = tenx_data_ensembl , topVar = 5000 , main = "10X: aNSC0 young and old Top variable genes" , annotation = filter(tenx_annotation , type %in% c("aNSC0")) , annotation_colors = list(age = age_colors , type = tenx_celltype_colors ) , cluster_cols = TRUE , cluster_rows = TRUE)
```

##### aNSC1: Top 5000 variable genes 

```{r}
# HeatmapGenes(x = tenx_data_ensembl , topVar = 5000 , main = "10X: aNSC1 young and old Top variable genes" , annotation = filter(tenx_annotation , type %in% c("aNSC1")) , annotation_colors = list(age = age_colors , type = tenx_celltype_colors ) , cluster_cols = TRUE , cluster_rows = TRUE)
```

##### aNSC2: Top 5000 variable genes

```{r}
# HeatmapGenes(x = tenx_data_ensembl , topVar = 5000 , main = "10X: aNSC2 young and old Top variable genes" , annotation = filter(tenx_annotation , type %in% c("aNSC2")) , annotation_colors = list(age = age_colors , type = tenx_celltype_colors ) , cluster_cols = TRUE , cluster_rows = TRUE)
```
##### TAP: Top 5000 variable genes

```{r}
# HeatmapGenes(x = tenx_data_ensembl , topVar = 5000 , main = "10X: TAP young and old Top variable genes" , annotation = filter(tenx_annotation , type %in% c("TAP")) , annotation_colors = list(age = age_colors , type = tenx_celltype_colors ) , cluster_cols = TRUE , cluster_rows = TRUE)
```

##### NB: Top 5000 variable genes

```{r}
# HeatmapGenes(x = tenx_data_ensembl , topVar = 5000 , main = "10X: NB young and old Top variable genes" , annotation = filter(tenx_annotation , type %in% c("NB")) , annotation_colors = list(age = age_colors , type = tenx_celltype_colors ) , cluster_cols = TRUE , cluster_rows = TRUE)
```

##### OPC: Top 5000 variable genes

```{r}
# HeatmapGenes(x = tenx_data_ensembl , topVar = 5000 , main = "10X: OPC young and old Top variable genes" , annotation = filter(tenx_annotation , type %in% c("OPC")) , annotation_colors = list(age = age_colors , type = tenx_celltype_colors ) , cluster_cols = TRUE , cluster_rows = TRUE)
```

##### OD: Top 5000 variable genes

```{r}
# HeatmapGenes(x = tenx_data_ensembl , topVar = 5000 , main = "10X: OD young and old Top variable genes" , annotation = filter(tenx_annotation , type %in% c("OD")) , annotation_colors = list(age = age_colors , type = tenx_celltype_colors ) , cluster_cols = TRUE , cluster_rows = TRUE)
```


## Compare pseudo-bulk Single Cell RNA-seq with bulk samples - mean TPM values

```{r}
Pseudobulk_bulk_compare <- function( pseudobulk_data = NULL , pseudobulk_celltype = "x" , bulk_celltype = "y" , bulk_data = NULL , annotation_pseudobulk = NULL , annotation_bulk = NULL , celltype_bulk = NULL  , main = "" ){
  require("ggplot2")
  require("dplyr")
  require("tibble")
  
  if( is.null(pseudobulk_data) | is.null(bulk_data) ){ stop("You need to supply all data !") }
  if( is.null(annotation_pseudobulk) | is.null(annotation_pseudobulk) ){stop("You need to supply the annotation !")}
  
  
  ## Strip Log2 transform of the data
  pseudobulk_data <- 2^(pseudobulk_data)-1
  bulk_data <- 2^(bulk_data)-1
  
  pseudobulk_data <- pseudobulk_data[!is.na(rownames(pseudobulk_data)),]
  bulk_data <- bulk_data[!is.na(rownames(bulk_data)),]
  
  pseudobulk_data <- pseudobulk_data[,annotation_pseudobulk$cell]
  bulk_data <- bulk_data[,annotation_bulk$sample]
    
  pseudobulk_data_comb <- data.frame( expr_x = log2(rowMeans(pseudobulk_data)+1) , ensembl_gene_id = rownames(pseudobulk_data) )
  bulk_data_comb <- data.frame( expr_y = log2(rowMeans(bulk_data)+1) , ensembl_gene_id = rownames(bulk_data) )

  x <- full_join(x = pseudobulk_data_comb , y = bulk_data_comb , by = "ensembl_gene_id" )
  
  g <- ggplot(data = x , mapping = aes(x = expr_x , y = expr_y)) + geom_point(alpha = 0.5) + coord_equal() + labs(x = pseudobulk_celltype , y = bulk_celltype ) + ggtitle(main)
  
  g
  }
```

### SMARTseq2

```{r}
Pseudobulk_bulk_compare(pseudobulk_data = smartseq2_data , bulk_data = bulk_data , annotation_pseudobulk = filter(smartseq2_annotation, type %in% c("qNSC1","qNSC2","aNSC1","aNSC2"), age == "young") , annotation_bulk = filter(bulk_annotation, type %in% c("NSC") , age == "young"), pseudobulk_celltype = "pseudo-bulk samples: NSC (log2(TPM+1))" , bulk_celltype = " bulk samples: NSC (log2(TPM+1))" , main = "Young NSC: pseudo-bulk vs bulk" )
```

```{r}
Pseudobulk_bulk_compare(pseudobulk_data = smartseq2_data , bulk_data = bulk_data , annotation_pseudobulk = filter(smartseq2_annotation, type %in% c("qNSC1","qNSC2","aNSC1","aNSC2"), age == "old") , annotation_bulk = filter(bulk_annotation, type %in% c("NSC") , age == "young"), pseudobulk_celltype = "pseudo-bulk samples: NSC (log2(TPM+1))" , bulk_celltype = " bulk samples: NSC (log2(TPM+1))" , main = "NSC: old pseudo-bulk (SMARTseq2) vs young bulk" )
```

```{r}
Pseudobulk_bulk_compare(pseudobulk_data = smartseq2_data , bulk_data = bulk_data , annotation_pseudobulk = filter(smartseq2_annotation, type %in% c("qNSC1","qNSC2","aNSC1","aNSC2"), age == "young") , annotation_bulk = filter(bulk_annotation, type %in% c("NSC") , age == "old"), pseudobulk_celltype = "pseudo-bulk samples: NSC (log2(TPM+1))" , bulk_celltype = " bulk samples: NSC (log2(TPM+1))" , main = "NSC: young pseudo-bulk (SMARTseq2) vs old bulk" )
```

```{r}
Pseudobulk_bulk_compare(pseudobulk_data = smartseq2_data , bulk_data = bulk_data , annotation_pseudobulk = filter(smartseq2_annotation, type %in% c("qNSC1","qNSC2","aNSC1","aNSC2"), age == "old") , annotation_bulk = filter(bulk_annotation, type %in% c("NSC") , age == "old"), pseudobulk_celltype = "pseudo-bulk samples: NSC (log2(TPM+1))" , bulk_celltype = " bulk samples: NSC (log2(TPM+1))" , main = "Old NSC: pseudo-bulk (SMARTseq2) vs bulk" )
```

### 10X

```{r}
Pseudobulk_bulk_compare(pseudobulk_data = tenx_data_ensembl , bulk_data = bulk_data , annotation_pseudobulk = filter(tenx_annotation, type %in% c("qNSC1","qNSC2","aNSC0","aNSC1","aNSC2"), age == "old") , annotation_bulk = filter(bulk_annotation, type %in% c("NSC") , age == "old"), pseudobulk_celltype = "pseudo-bulk samples: NSC (log2(expression+1))" , bulk_celltype = " bulk samples: NSC (log2(TPM+1))" , main = "Old NSC: pseudo-bulk (10X) vs bulk" )
```

```{r}
Pseudobulk_bulk_compare(pseudobulk_data = tenx_data_ensembl , bulk_data = bulk_data , annotation_pseudobulk = filter(tenx_annotation, type %in% c("qNSC1","qNSC2","aNSC0","aNSC1","aNSC2"), age == "old") , annotation_bulk = filter(bulk_annotation, type %in% c("NSC") , age == "young"), pseudobulk_celltype = "pseudo-bulk samples: NSC (log2(expression+1))" , bulk_celltype = " bulk samples: NSC (log2(TPM+1))" , main = "NSC: Old pseudo-bulk (10X) vs young bulk" )
```

```{r}
Pseudobulk_bulk_compare(pseudobulk_data = tenx_data_ensembl , bulk_data = bulk_data , annotation_pseudobulk = filter(tenx_annotation, type %in% c("qNSC1","qNSC2","aNSC0","aNSC1","aNSC2"), age == "young") , annotation_bulk = filter(bulk_annotation, type %in% c("NSC") , age == "old"), pseudobulk_celltype = "pseudo-bulk samples: NSC (log2(TPM+1))" , bulk_celltype = " bulk samples: NSC (log2(expression+1))" , main = "NSC: Young pseudo-bulk (10X) vs old bulk" )
```

```{r}
Pseudobulk_bulk_compare(pseudobulk_data = tenx_data_ensembl , bulk_data = bulk_data , annotation_pseudobulk = filter(tenx_annotation, type %in% c("qNSC1","qNSC2","aNSC0","aNSC1","aNSC2"), age == "young") , annotation_bulk = filter(bulk_annotation, type %in% c("NSC") , age == "young"), pseudobulk_celltype = "pseudo-bulk samples: NSC (log2(TPM+1))" , bulk_celltype = " bulk samples: NSC (log2(expression+1))" , main = "Young NSC: pseudo-bulk (10X) vs bulk" )
```

<hr>

## Pseudobulk MA Plot

```{r}
Pseudobulk_bulk_MAPlot <- function( pseudobulk_data = NULL , annotation_pseudobulk = NULL  , main = "" , labeL_top_genes = FALSE , highlight_genes = c() , max_foldChange = 10  , max_baseMean = 1000000 ){
  require("ggplot2")
  require("dplyr")
  require("tibble")
  
  ## Strip Log2 transform of the data
  pseudobulk_data <- 2^(pseudobulk_data) - 1
  bulk_data <- 2^(bulk_data) - 1

  if( is.null(pseudobulk_data) ){ stop("You need to supply all data !") }
  if( is.null(annotation_pseudobulk) ){stop("You need to supply the annotation !")}
  
  annotation_pseudobulk_old <- filter(annotation_pseudobulk , age == "old")
  annotation_pseudobulk_young <- filter(annotation_pseudobulk , age == "young")

  pseudobulk_data <- pseudobulk_data[!is.na(rownames(pseudobulk_data)),]
    
  pseudobulk_data_old <- pseudobulk_data[,as.character(annotation_pseudobulk_old$cell)]
  pseudobulk_data_young <- pseudobulk_data[,as.character(annotation_pseudobulk_young$cell)]
  
  pseudobulk_data_complete <- pseudobulk_data[, c( as.character(annotation_pseudobulk_old$cell),as.character(annotation_pseudobulk_young$cell)) ]
  
  if( ! all(rownames(pseudobulk_data_old) == rownames(pseudobulk_data_young))){stop("Rownames are not correct: Pseudobulk")}

  pseudobulk_data_comb <- data.frame( log2FoldChange = log2(((rowSums(pseudobulk_data_old) + 1) / (rowSums(pseudobulk_data_young) + 1) ) )  , expr_young_pseudobulk = rowSums(pseudobulk_data_young) , baseMean = rowMeans(pseudobulk_data_complete),  expr_old_pseudobulk = rowSums(pseudobulk_data_old) , gene = rownames(pseudobulk_data_old) )
  
  pseudobulk_data_comb$log2FoldChange[is.nan(pseudobulk_data_comb$log2FoldChange)] <- 0
  
  x_highlight <- filter(pseudobulk_data_comb ,gene %in% highlight_genes )
  
  g <- ggplot(data = pseudobulk_data_comb , mapping = aes(x = baseMean , y = log2FoldChange)) +  labs(x = "baseMean"  , y = "Log2FoldChange" ) + ggtitle(main) + geom_vline(xintercept = 0) + geom_hline(yintercept = 0) + geom_point(color = "grey" ) + geom_point(data = x_highlight , color = "black", fill = "cyan"  , size = 2 , pch = 21) 
  
  if( labeL_top_genes ){
      g <- g + geom_text_repel(data = x_label , mapping = aes( x = baseMean , y = log2FoldChange , label = gene)) 
  }
  
  g <- g + scale_x_log10(limits = c(0.00001, max_baseMean ), expand = c(0, 0)) + ggtitle(main) + ylim(c(-max_foldChange,max_foldChange))
  
  g
  }
```

```{r fig.width=15, fig.height=15}
Pseudobulk_bulk_MAPlot(pseudobulk_data = smartseq2_data , annotation_pseudobulk = smartseq2_annotation)
```

```{r fig.width=15, fig.height=15}
Pseudobulk_bulk_MAPlot(pseudobulk_data = tenx_data , annotation_pseudobulk = dplyr::filter(tenx_annotation, type %in% c("qNSC1","qNSC2","aNSC0","aNSC1","aNSC2")) )
```


# Plot individual genes in the single cell datasets

## Beeswarm plot of Il33

### SMARTseq2

```{r}
IL33_smartseq2_data <- smartseq2_data["ENSMUSG00000024810",]
```

```{r}
IL33_smartseq2_df <- data.frame( expression = IL33_smartseq2_data , row.names = names(IL33_smartseq2_data)  ) %>% rownames_to_column("cell") %>% left_join( smartseq2_annotation , by = "cell")
```

```{r}
addmargins( table( IL33_smartseq2_df$age  , IL33_smartseq2_df$expression > 2.5 ) )
```

```{r fig.width=9 , fig.height=5}
library(ggbeeswarm)

ggplot(data = IL33_smartseq2_df , mapping = aes(x = age , y = expression , color = type )) + geom_quasirandom( size = 1.5 ,method = "smiley" ) + xlab("Age") + ylab("log2(TPM+1)") + ggtitle("Expression of Il33 in the SMARTseq2 data") + scale_x_discrete(labels = c(young = "2 month" , old = "22 month") ) + scale_color_manual( name = "Activation state" , values = smartseq2_celltype_colors ) + guides(color = guide_legend(override.aes=list(size=1.5)))
```


### 10X

```{r}
IL33_10X_data <- tenx_data_ensembl["ENSMUSG00000024810",]
```

```{r}
tenx_annotation_nscs <- tenx_annotation %>% filter( type %in% c("qNSC1","qNSC2","aNSC0","aNSC1","aNSC2"))

IL33_10X_df <- data.frame( expression = IL33_10X_data , row.names = names(IL33_10X_data)  ) %>% rownames_to_column("cell") %>% filter(cell %in% as.character( tenx_annotation_nscs$cell ) ) %>% left_join( tenx_annotation_nscs , by = "cell")
```

```{r}
addmargins( table( IL33_10X_df$age  , IL33_10X_df$expression > 0.5 ) )
```

```{r fig.width=9 , fig.height=5}
library(ggbeeswarm)

ggplot(data = IL33_10X_df , mapping = aes(x = age , y = expression , color = type )) + xlab("Age") + ylab("log2(expression+1)") + ggtitle("Expression of Il33 in the 10X data") + scale_x_discrete(labels = c(young = "2 month" , old = "22 month") ) + scale_color_manual( name = "Activation state" , values = tenx_celltype_colors ) + guides(color = guide_legend(override.aes=list(size=1.5))) + geom_quasirandom( size = 0.5 ,method = "smiley" ) 
```

<hr>

# Search for genes from Gene Lists in all datasets

## InnateDB genes - manually curated from the bulk seq data (from sig. genes with log2FoldChange > 1)

```{r}
DE_genes_NSCs_bulk_up_InnateDB <- read.csv("GeneLists/Manually_curated_DE_genes_from_bulk/InnateDB_Gene_list/InnateDB_NSCs.csv" , stringsAsFactors = FALSE , header = TRUE)

library(biomaRt)
mart <- useMart(biomart = "ensembl", dataset = "mmusculus_gene_ensembl")
genes_symbol_ensembl <- getBM(attributes = c("mgi_symbol","ensembl_gene_id" ), filters = "mgi_symbol" , values = DE_genes_NSCs_bulk_up_InnateDB$Genes , mart = mart  )
genes_symbol_ensembl <- dplyr::rename(genes_symbol_ensembl , MGI.symbol = mgi_symbol , gene = ensembl_gene_id )

genes_symbol_ensembl <-  dplyr::filter( genes_symbol_ensembl , gene %in% as.character(bulk_DE_genes_NSC %>% filter(padj < 0.05 , log2FoldChange > 1) %>% pull("ensembl_gene_id") ) )

DE_genes_NSCs_bulk_up_InnateDB <- genes_symbol_ensembl
```

```{r}
gene_set <- DE_genes_NSCs_bulk_up_InnateDB
```

```{r}
plot_title <- "NSC bulk seq (top DE genes between old and young) annotated in InnateDB"
```


### Bulk {.tabset .tabset-fade .tabset-pills}

#### Bulk sequencing: Heatmap


```{r fig.width=5, fig.height=5}
HeatmapGenes(x = bulk_data , genes = unique(gene_set$gene) , main = paste("Bulk:", plot_title) , annotation = bulk_annotation , show_colnames = FALSE , annotation_colors = list(age = age_colors_bulk ) )
```

#### Bulk sequencing: MA-Plot {.tabset .tabset-fade .tabset-pills}

##### NSC

```{r fig.width=15}
MAPlot(x = bulk_DE_genes_NSC , main = "NSC (19 month vs 2 month)" , highlight_genes = unique(gene_set$MGI.symbol) ) 
```

```{r fig.width=15}
MAPlot(x = bulk_DE_genes_NSC_Mo7_vs_young , main = "NSC (7 Month vs 2 Month)" , highlight_genes = unique(gene_set$MGI.symbol) ) 
```

##### NB

```{r fig.width=15}
MAPlot(x = bulk_DE_genes_NB , main = "NB" , highlight_genes = unique(gene_set$MGI.symbol) ) 
```

##### Microglia

```{r fig.width=15}
MAPlot(x = bulk_DE_genes_Microglia , main = "Microglia" , highlight_genes = unique(gene_set$MGI.symbol) ) 
```

##### Endothelial cells

```{r fig.width=15}
MAPlot(x = bulk_DE_genes_Endothelial , main = "Endothelial" , highlight_genes = unique(gene_set$MGI.symbol) ) 
```

##### Pseudobulk  SMARTseq2

```{r fig.width=15, fig.height=15}
Pseudobulk_bulk_MAPlot(pseudobulk_data = smartseq2_data , annotation_pseudobulk = smartseq2_annotation , highlight_genes = unique(gene_set$gene) , main = "Pseudobulk: SMARTseq2")
```

##### Pseudobulk 10X

```{r fig.width=15, fig.height=15}
Pseudobulk_bulk_MAPlot(pseudobulk_data = tenx_data , annotation_pseudobulk = dplyr::filter(tenx_annotation, type %in% c("qNSC1","qNSC2","aNSC0","aNSC1","aNSC2")) , highlight_genes = unique(gene_set$MGI.symbol) , main = "Pseudobulk: 10X")
```


### SMARTseq2 {.tabset .tabset-fade .tabset-pills}

#### SMARTseq2 sequencing: Heatmap

##### Arranged by celltype {.tabset .tabset-fade .tabset-pills}

```{r}
 HeatmapGenes(x = smartseq2_data , genes = unique(gene_set$gene) , main = paste("SMARTseq2:", plot_title) , annotation = smartseq2_annotation , annotation_colors = list(age = age_colors , type = smartseq2_celltype_colors )  )
```

##### Columns clustered

```{r fig.width=10.1 , fig.height=10.2}
 test <- HeatmapGenes(x = smartseq2_data , genes = unique(gene_set$gene) , main = paste("SMARTseq2:", plot_title) , annotation = smartseq2_annotation %>% dplyr::filter(type %in% c("qNSC1","qNSC2")) , annotation_colors = list(age = age_colors , type = smartseq2_celltype_colors ) , cluster_cols = TRUE )
```



### 10X {.tabset .tabset-fade .tabset-pills}

#### 10X sequencing: Heatmap

```{r fig.width=10.1, fig.height=10.2}
HeatmapGenes(x = tenx_data_ensembl , genes = unique(gene_set$gene) , main = paste("10X:", plot_title) , annotation = tenx_annotation , annotation_colors = list(age = age_colors , type = tenx_celltype_colors ) )
```

```{r fig.width=10.1, fig.height=10.2}
HeatmapGenes(x = tenx_data_ensembl , genes = unique(gene_set$gene) , main = paste("10X:", plot_title) , annotation = tenx_annotation , annotation_colors = list(age = age_colors , type = tenx_celltype_colors ) , cluster_cols = TRUE )
```


##  InnateDB genes - manually curated from the bulk seq data (from sig. genes with log2FoldChange > 1) - from Endothelial, NB and Microglia

### Endothelial

```{r}
DE_genes_Endothelial_bulk_up_InnateDB <- read.csv("GeneLists/Manually_curated_DE_genes_from_bulk/InnateDB_Gene_list/InnateDB_Endos.csv" , stringsAsFactors = FALSE , header = TRUE)

library(biomaRt)
mart <- useMart(biomart = "ensembl", dataset = "mmusculus_gene_ensembl")
genes_symbol_ensembl <- getBM(attributes = c("mgi_symbol","ensembl_gene_id" ), filters = "mgi_symbol" , values = DE_genes_Endothelial_bulk_up_InnateDB$Genes , mart = mart  )
genes_symbol_ensembl <- dplyr::rename(genes_symbol_ensembl , MGI.symbol = mgi_symbol , gene = ensembl_gene_id )

genes_symbol_ensembl <-  dplyr::filter( genes_symbol_ensembl , gene %in% as.character(bulk_DE_genes_Endothelial %>% filter(padj < 0.05 , log2FoldChange > 1) %>% pull("ensembl_gene_id") ) )

DE_genes_Endothelial_bulk_up_InnateDB <- genes_symbol_ensembl
```

```{r fig.width=15}
MAPlot(x = bulk_DE_genes_Endothelial , main = "Endothelial" , highlight_genes = unique(DE_genes_Endothelial_bulk_up_InnateDB$MGI.symbol) ) 
```

### Microglia

```{r}
DE_genes_Microglia_bulk_up_InnateDB <- read.csv("GeneLists/Manually_curated_DE_genes_from_bulk/InnateDB_Gene_list/InnateDB_Microglia.csv" , stringsAsFactors = FALSE , header = TRUE)

library(biomaRt)
mart <- useMart(biomart = "ensembl", dataset = "mmusculus_gene_ensembl")
genes_symbol_ensembl <- getBM(attributes = c("mgi_symbol","ensembl_gene_id" ), filters = "mgi_symbol" , values = DE_genes_Microglia_bulk_up_InnateDB$Genes , mart = mart  )
genes_symbol_ensembl <- dplyr::rename(genes_symbol_ensembl , MGI.symbol = mgi_symbol , gene = ensembl_gene_id )

genes_symbol_ensembl <-  dplyr::filter( genes_symbol_ensembl , gene %in% as.character(bulk_DE_genes_Microglia %>% dplyr::filter(padj < 0.05 , log2FoldChange > 1) %>% pull("ensembl_gene_id") ) )

DE_genes_Microglia_bulk_up_InnateDB <- genes_symbol_ensembl
```

```{r fig.width=15}
MAPlot(x = bulk_DE_genes_Microglia , main = "Microglia" , highlight_genes = unique(DE_genes_Microglia_bulk_up_InnateDB$MGI.symbol) ) 
```

### NB

```{r}
DE_genes_NB_bulk_up_InnateDB <- read.csv("GeneLists/Manually_curated_DE_genes_from_bulk/InnateDB_Gene_list/InnateDB_Neuroblasts.csv" , stringsAsFactors = FALSE , header = TRUE)

library(biomaRt)
mart <- useMart(biomart = "ensembl", dataset = "mmusculus_gene_ensembl")
genes_symbol_ensembl <- getBM(attributes = c("mgi_symbol","ensembl_gene_id" ), filters = "mgi_symbol" , values = DE_genes_NB_bulk_up_InnateDB$Genes , mart = mart  )
genes_symbol_ensembl <- dplyr::rename(genes_symbol_ensembl , MGI.symbol = mgi_symbol , gene = ensembl_gene_id )

genes_symbol_ensembl <-  dplyr::filter( genes_symbol_ensembl , gene %in% as.character(bulk_DE_genes_NB %>% dplyr::filter(padj < 0.05 , log2FoldChange > 1) %>% pull("ensembl_gene_id") ) )

DE_genes_NB_bulk_up_InnateDB <- genes_symbol_ensembl
```

```{r fig.width=15}
MAPlot(x = bulk_DE_genes_NB , main = "Neuroblast" , highlight_genes = unique(DE_genes_NB_bulk_up_InnateDB$MGI.symbol) ) 
```

# Load raw counts

We can also compare the raw counts for specific genes that we recover with the different sequencing methods

## Bulk sequencing data

```{r}
data_bulk <- read.csv(file = "raw_counts/compatible_rownames_only_NSCs/Bulk_seq_All.genes.counts_compat.csv" , row.names = 1)

anno_bulk <- read.csv(file = "raw_counts/annotation_bulk.csv" , row.names = 1)
```

Check if all cells from the annotation are present in the data as columns and the other way around

```{r}
all(colnames(data_bulk) == anno_bulk$name)
```


## SMARTseq2 data

```{r}
data_SMARTseq2_NSCs <- read.csv(file = "raw_counts/compatible_rownames_only_NSCs/SMARTseq2_All.genes.counts_compat.csv" , row.names = 1)

anno_SMARTseq2_NSCs <- read.csv(file = "raw_counts/annotation_NSCs_SMARTseq2.csv" , row.names = 1)
```

Check if all cells from the annotation are present in the data as columns and the other way around

```{r}
all(colnames(data_SMARTseq2_NSCs) == anno_SMARTseq2_NSCs$name)
```

## 10X data

```{r}
data_10X_NSCs <- read.csv(file = "raw_counts/compatible_rownames_only_NSCs/10X_All.genes.counts_compat.csv" , row.names = 1)

anno_10X_NSCs <- read.csv(file = "raw_counts/annotation_10x_NSCs.csv" , row.names = 1)
```

```{r}
colnames(data_10X_NSCs) <- sub(x = colnames(data_10X_NSCs) , pattern = "\\." , replacement = "-" )
```


Check if all cells from the annotation are present in the data as columns and the other way around

```{r}
all(colnames(data_10X_NSCs) == anno_10X_NSCs$name)
```

# Check raw counts for highly upregulated genes in bulk NSC old to young comparison

## 10X

How many counts do genes have in the Single cell data sets, that are significantly upregulated in the bulk NSCs

```{r}
cells_NSCs_10X <- as.character(anno_10X_NSCs$name)
```


### Iigp1 - ENSMUSG00000054072

```{r}
Iigp1_df <- data.frame( totalReads = colSums(data_10X_NSCs) , geneReads = as.numeric(data_10X_NSCs["ENSMUSG00000054072",cells_NSCs_10X]) , gene = "Iigp1"   )  %>% rownames_to_column("name") %>% left_join(anno_10X_NSCs , by = "name" )
```

```{r}
addmargins( table( Iigp1_df$age  , Iigp1_df$geneReads > 0 ) )
```

```{r fig.width=6 , fig.height=2}
gg_Iigp1 <- ggplot(data = Iigp1_df , mapping = aes(x = totalReads , y = geneReads ) ) + geom_point(aes(color = age)) + scale_color_manual(values = c("old" = "slateblue" , "young" = "yellowgreen") )

gg_Iigp1
```

### Ddx60 - ENSMUSG00000037921

```{r}
Ddx60_df <- data.frame( totalReads = colSums(data_10X_NSCs) , geneReads = as.numeric(data_10X_NSCs["ENSMUSG00000037921",cells_NSCs_10X]) , gene = "Ddx60"   ) %>% rownames_to_column("name") %>% left_join(anno_10X_NSCs , by = "name" )
```

```{r}
addmargins( table( Ddx60_df$age  , Ddx60_df$geneReads > 0 ) )
```

```{r fig.width=6 , fig.height=2}
gg_Ddx60 <- ggplot(data = Ddx60_df , mapping = aes(x = totalReads , y = geneReads ) ) + geom_point(aes(color = age)) + scale_color_manual(values = c("old" = "slateblue" , "young" = "yellowgreen") )
```

### Ifit1 - ENSMUSG00000078920

```{r}
Ifit1_df <- data.frame( totalReads = colSums(data_10X_NSCs) , geneReads = as.numeric(data_10X_NSCs["ENSMUSG00000078920",cells_NSCs_10X]) , gene = "Ifit1" ) %>% rownames_to_column("name") %>% left_join(anno_10X_NSCs , by = "name" )
```

```{r}
addmargins( table( Ifit1_df$age  , Ifit1_df$geneReads > 0 ) )
```

```{r fig.width=6 , fig.height=2}
gg_Ifit1 <- ggplot(data = Ifit1_df , mapping = aes(x = totalReads , y = geneReads ) ) + geom_point(aes(color = age)) + scale_color_manual(values = c("old" = "slateblue" , "young" = "yellowgreen") )

gg_Ifit1
```

### Rsad2 - ENSMUSG00000020641

```{r}
Rsad2_df <- data.frame( totalReads = colSums(data_10X_NSCs) , geneReads = as.numeric(data_10X_NSCs["ENSMUSG00000020641",cells_NSCs_10X]) , gene = "Rsad2"  ) %>% rownames_to_column("name") %>% left_join(anno_10X_NSCs , by = "name" )
```

```{r}
addmargins( table( Rsad2_df$age  , Rsad2_df$geneReads > 0 ) )
```

```{r fig.width=6 , fig.height=2}
gg_Rsad2 <- ggplot(data = Rsad2_df , mapping = aes(x = totalReads , y = geneReads ) ) + geom_point(aes(color = age)) + scale_color_manual(values = c("old" = "slateblue" , "young" = "yellowgreen") )

gg_Rsad2
```

### C3 - ENSMUSG00000024164

```{r}
C3_df <- data.frame( totalReads = colSums(data_10X_NSCs) , geneReads = as.numeric(data_10X_NSCs["ENSMUSG00000024164",cells_NSCs_10X]) , gene = "C3"   )  %>% rownames_to_column("name") %>% left_join(anno_10X_NSCs , by = "name" )
```

```{r}
addmargins( table( C3_df$age  , C3_df$geneReads > 0 ) )
```

```{r fig.width=6 , fig.height=2}
gg_C3 <- ggplot(data = C3_df , mapping = aes(x = totalReads , y = geneReads ) ) + geom_point(aes(color = age)) + scale_color_manual(values = c("old" = "slateblue" , "young" = "yellowgreen") )

gg_C3
```



### Ifi44 - ENSMUSG00000028037

```{r}
Ifi44_df <- data.frame( totalReads = colSums(data_10X_NSCs) , geneReads = as.numeric(data_10X_NSCs["ENSMUSG00000028037",cells_NSCs_10X]) , gene = "Ifi44"   )  %>% rownames_to_column("name") %>% left_join(anno_10X_NSCs , by = "name" )
```

```{r}
addmargins( table( Ifi44_df$age  , Ifi44_df$geneReads > 0 ) )
```

```{r fig.width=6 , fig.height=2}
gg_Ifi44 <- ggplot(data = Ifi44_df , mapping = aes(x = totalReads , y = geneReads ) ) + geom_point(aes(color = age)) + scale_color_manual(values = c("old" = "slateblue" , "young" = "yellowgreen") )

gg_Ifi44
```


### Cxcl10 - ENSMUSG00000034855

```{r}
Cxcl10_df <- data.frame( totalReads = colSums(data_10X_NSCs) , geneReads = as.numeric(data_10X_NSCs["ENSMUSG00000034855",cells_NSCs_10X]) , gene = "Cxcl10"   )  %>% rownames_to_column("name") %>% left_join(anno_10X_NSCs , by = "name" )
```
```{r}
addmargins( table( Cxcl10_df$age  , Cxcl10_df$geneReads > 0 ) )
```

```{r fig.width=6 , fig.height=2}
gg_Cxcl10 <- ggplot(data = Cxcl10_df , mapping = aes(x = totalReads , y = geneReads ) ) + geom_point(aes(color = age)) + scale_color_manual(values = c("old" = "slateblue" , "young" = "yellowgreen") )

gg_Cxcl10
```

### Il33 - ENSMUSG00000024810

```{r}
Il33_df <- data.frame( totalReads = colSums(data_10X_NSCs) , geneReads = as.numeric(data_10X_NSCs["ENSMUSG00000024810",cells_NSCs_10X]) , gene = "Il33"   )  %>% rownames_to_column("name") %>% left_join(anno_10X_NSCs , by = "name" )
```

```{r}
addmargins( table( Il33_df$age  , Il33_df$geneReads > 0 ) )
```

```{r fig.width=6 , fig.height=2}
gg_Il33 <- ggplot(data = Il33_df , mapping = aes(x = totalReads , y = geneReads ) ) + geom_point(aes(color = age)) + scale_color_manual(values = c("old" = "slateblue" , "young" = "yellowgreen") )

gg_Il33
```



### Ifi47 - ENSMUSG00000078920

```{r}
Ifi47_df <- data.frame( totalReads = colSums(data_10X_NSCs) , geneReads = as.numeric(data_10X_NSCs["ENSMUSG00000078920",cells_NSCs_10X]) , gene = "Ifi47"   ) %>% rownames_to_column("name") %>% left_join(anno_10X_NSCs , by = "name" )
```

```{r}
addmargins( table( Ifi47_df$age  , Ifi47_df$geneReads > 0 ) )
```

```{r fig.width=6 , fig.height=2}
gg_Ifi47 <- ggplot(data = Ifi47_df , mapping = aes(x = totalReads , y = geneReads ) ) + geom_point(aes(color = age)) + scale_color_manual(values = c("old" = "slateblue" , "young" = "yellowgreen") )

gg_Ifi47
```

### Gm4951 - ENSMUSG00000073555

```{r}
Gm4951_df <- data.frame( totalReads = colSums(data_10X_NSCs) , geneReads = as.numeric(data_10X_NSCs["ENSMUSG00000073555",cells_NSCs_10X]) , gene = "Gm4951"   ) %>% rownames_to_column("name") %>% left_join(anno_10X_NSCs , by = "name" )
```

```{r}
addmargins( table( Gm4951_df$age  , Gm4951_df$geneReads > 0 ) )
```

```{r fig.width=6 , fig.height=2}
gg_Gm4951 <- ggplot(data = Gm4951_df , mapping = aes(x = totalReads , y = geneReads ) ) + geom_point(aes(color = age)) + scale_color_manual(values = c("old" = "slateblue" , "young" = "yellowgreen") )

gg_Gm4951
```

### Slc1a3 - ENSMUSG00000005360

```{r}
Slc1a3_df <- data.frame( totalReads = colSums(data_10X_NSCs) , geneReads = as.numeric(data_10X_NSCs["ENSMUSG00000005360",cells_NSCs_10X]) , gene = "Slc1a3"   ) %>% rownames_to_column("name") %>% left_join(anno_10X_NSCs , by = "name" )
```

```{r}
addmargins( table( Slc1a3_df$age  , Slc1a3_df$geneReads > 0 ) )
```

```{r fig.width=6 , fig.height=2}
gg_Slc1a3 <- ggplot(data = Slc1a3_df , mapping = aes(x = totalReads , y = geneReads ) ) + geom_point(aes(color = age)) + scale_color_manual(values = c("old" = "slateblue" , "young" = "yellowgreen") )

gg_Slc1a3
```

### Sox2 - ENSMUSG00000074637

```{r}
Sox2_df <- data.frame( totalReads = colSums(data_10X_NSCs) , geneReads = as.numeric(data_10X_NSCs["ENSMUSG00000074637",cells_NSCs_10X]) , gene = "Sox2"   ) %>% rownames_to_column("name") %>% left_join(anno_10X_NSCs , by = "name" )
```

```{r}
addmargins( table( Sox2_df$age  , Sox2_df$geneReads > 0 ) )
```


```{r fig.width=6 , fig.height=2}
gg_Sox2 <- ggplot(data = Sox2_df , mapping = aes(x = totalReads , y = geneReads ) ) + geom_point(aes(color = age)) + scale_color_manual(values = c("old" = "slateblue" , "young" = "yellowgreen") )

gg_Sox2
```

```{r}
interferon_genes_bulk_df <- bind_rows( Ifit1_df , C3_df , Rsad2_df , Iigp1_df , Ifi47_df , Il33_df , Cxcl10_df , Sox2_df , Slc1a3_df )
```

### Combine all genes

#### Scatterplot

```{r}
gg_interferon_genes <- ggplot(data = interferon_genes_bulk_df , mapping = aes(x = totalReads , y = geneReads ) ) + geom_point(aes(color = age)) + scale_color_manual(values = c("old" = "slateblue" , "young" = "yellowgreen") ) + facet_grid( gene ~ . )
```


```{r fig.width=10 , fig.height=12}
gg_interferon_genes
```

#### Barplot

Show the same data as stacked barplots

```{r}
interferon_genes_bulk_df_bars <- interferon_genes_bulk_df %>% mutate( interval = base::cut( geneReads , breaks = c(-0.1, 0.5,5.5,50.5,Inf) , labels = c("0","1-5","5-50","50-") ) ) %>% group_by(interval,gene,age) %>% summarise( counts = n() ) %>% ungroup() %>% mutate( interval = fct_rev( interval ) , age = fct_recode( .f = age ,  "22 month" = "old",  "2 month" = "young" ) %>% fct_rev() ) %>% group_by(gene,age) %>% mutate( Percent = 100*counts/sum(counts) )
```

```{r fig.width=18, fig.height=5}
ggplot( data = interferon_genes_bulk_df_bars , mapping = aes(x = age , y = Percent , fill = interval) ) + geom_bar(stat = "identity") + facet_grid(facets = . ~ gene ) + viridis::scale_fill_viridis( discrete = TRUE , direction = -1 ) + ggtitle("Read counts: 10X")
```


### Bulk NSC genes in InnateDB

Which genes are the ones with high fold changes in the bulk and are annotated in InnateDB

```{r}
innateDB_genes_NSCs_bulk_up_table <- structure(list(ensembl_gene_id = structure(c(24L, 25L, 9L, 40L, 
34L, 15L, 17L, 31L, 22L, 30L, 26L, 20L, 19L, 33L, 27L, 4L, 16L, 
8L, 7L, 29L, 35L, 1L, 12L, 2L, 32L, 13L, 36L, 10L, 3L, 28L, 21L, 
39L, 23L, 11L, 14L, 6L, 5L, 18L, 38L, 37L), .Label = c("ENSMUSG00000000386", 
"ENSMUSG00000002325", "ENSMUSG00000005413", "ENSMUSG00000014361", 
"ENSMUSG00000015766", "ENSMUSG00000017716", "ENSMUSG00000018899", 
"ENSMUSG00000020122", "ENSMUSG00000020641", "ENSMUSG00000021508", 
"ENSMUSG00000022306", "ENSMUSG00000023951", "ENSMUSG00000024066", 
"ENSMUSG00000024079", "ENSMUSG00000024164", "ENSMUSG00000025492", 
"ENSMUSG00000025498", "ENSMUSG00000026104", "ENSMUSG00000026896", 
"ENSMUSG00000028270", "ENSMUSG00000029167", "ENSMUSG00000029826", 
"ENSMUSG00000030341", "ENSMUSG00000034459", "ENSMUSG00000034855", 
"ENSMUSG00000035692", "ENSMUSG00000036905", "ENSMUSG00000036913", 
"ENSMUSG00000037860", "ENSMUSG00000038418", "ENSMUSG00000038642", 
"ENSMUSG00000040747", "ENSMUSG00000046688", "ENSMUSG00000046718", 
"ENSMUSG00000053113", "ENSMUSG00000054717", "ENSMUSG00000060441", 
"ENSMUSG00000064215", "ENSMUSG00000074151", "ENSMUSG00000074896"
), class = "factor"), baseMean = c(1424.95976205735, 146.693384980886, 
254.747257624993, 1378.74240836277, 232.679222864689, 53.3856147003376, 
127.295243864467, 100.858827865452, 1254.48226869821, 8484.55808366433, 
133.391584778487, 449.698563109268, 435.970338794981, 487.652611748752, 
38.1324871848669, 2270.44585158928, 392.455793416614, 8123.4459446361, 
495.513923324155, 147.634742373719, 285.437837120322, 20.0485104704833, 
243.691996897216, 513.924995107202, 17.4190622356759, 18.1436595206331, 
15890.0096695135, 1952.66573537053, 149.127545170108, 284.046380761723, 
1257.30518127357, 52.8851930781209, 761.966603230871, 315.283958297394, 
633.932254776546, 1091.87572537937, 450.61462668542, 1757.25022917456, 
2425.56654256373, 64.6090952815893), log2FoldChange = c(4.55242859938374, 
6.95678411131362, 5.03641775134329, 3.37966257532159, 3.73428610778296, 
5.53141829606081, 3.86121962444797, 3.75531459127234, 2.42356052609584, 
2.16374238670928, 3.1971506351276, 2.44063114014779, 2.44677858670807, 
2.0893762261114, 3.67190586863129, 1.88755481055364, 2.10038800171168, 
1.69845072572928, 1.91183744819457, 2.41403420088619, 2.0284929496982, 
3.7014584495119, 1.94105240967367, 1.98333192338338, 3.4448344969081, 
3.44363682239329, 1.57281037684989, 1.63883482734508, 2.11717369111306, 
1.8668146256893, 1.43386187193389, 2.76038968668686, 1.7580616439951, 
1.52810896028218, 1.55394451633379, 1.28056799567681, 1.42242513375296, 
1.26735536508083, 1.18570808766668, 2.12810319958397), lfcSE = c(0.519384243203232, 
0.81388488255093, 0.641951444263734, 0.48612175786269, 0.578514935508735, 
0.878589568240684, 0.670578673894941, 0.684535958284751, 0.448804291965827, 
0.41379046101385, 0.645816741770005, 0.504575112575078, 0.531966043635904, 
0.460946382208802, 0.845572909706191, 0.436183111157877, 0.505011402058278, 
0.415807705611759, 0.488183380722589, 0.618808403224005, 0.520676205372646, 
0.965738215947367, 0.521153307010798, 0.553367834874403, 0.973891191367301, 
0.97448322263972, 0.448895240692065, 0.468806743609123, 0.606652875776758, 
0.541455272040843, 0.42118712809647, 0.818497304894856, 0.535426580260121, 
0.488159657804439, 0.500868295142623, 0.422276770181993, 0.470630833485906, 
0.427622347529472, 0.406142195700318, 0.7298433146452), stat = c(8.76504949651004, 
8.54762664900375, 7.84548083246336, 6.95229645795079, 6.45495194432465, 
6.29579327596285, 5.75804118854651, 5.48592743130992, 5.40003865711778, 
5.2290774934922, 4.95055397041132, 4.83700261729543, 4.59950144558989, 
4.53279666953747, 4.34250651420131, 4.32743671698567, 4.1590902564796, 
4.08470238239196, 3.91622804808462, 3.90110119434225, 3.89588179518673, 
3.83277619999832, 3.7245324620639, 3.58411132413133, 3.53718621489091, 
3.53380821997635, 3.50373591492099, 3.49575779292008, 3.48992607741657, 
3.44777255312879, 3.40433450189738, 3.37250919481214, 3.28347846149326, 
3.13034667214217, 3.10250125912102, 3.03253241973247, 3.02237981990518, 
2.96372575568792, 2.91944077768661, 2.91583571005033), pvalue = c(1.86694618820708e-18, 
1.25645385522089e-17, 4.31297720037574e-15, 3.59387157612209e-12, 
1.08253248479473e-10, 3.05831732678035e-10, 8.50955946071268e-09, 
4.11305732780317e-08, 6.66265384184588e-08, 1.70357951680874e-07, 
7.40025308679816e-07, 1.31811693167268e-06, 4.23503254113743e-06, 
5.82078401153992e-06, 1.40866307399552e-05, 1.50854667946568e-05, 
3.19517635376871e-05, 4.41333464318532e-05, 8.99451714694149e-05, 
9.57561097095347e-05, 9.78421013854258e-05, 0.000126705214745366, 
0.000195677532925451, 0.000338227814291896, 0.000404414420179005, 
0.000409618180066453, 0.0004587800815838, 0.000472717577863198, 
0.000483154175333289, 0.000565229800758029, 0.000663254779320521, 
0.000744865993665538, 0.00102534479496883, 0.00174600119776919, 
0.00191892731640661, 0.00242511046065225, 0.00250795646620197, 
0.00303939026229686, 0.00350660015484411, 0.00354737241964209
), padj = c(2.76849450249227e-15, 1.43322724762081e-14, 3.55317438357621e-12, 
1.15855481744162e-09, 2.43225366924561e-08, 5.96734047879288e-08, 
1.10691453721849e-06, 4.09345819557002e-06, 6.02442035492272e-06, 
1.40346559193094e-05, 5.05706695963733e-05, 7.72583240307279e-05, 
0.00020864218455989, 0.000272291501915222, 0.000555143310344139, 
0.00058560834318839, 0.00109173433525429, 0.00143520481192533, 
0.00261017015209384, 0.00274124971212874, 0.00278483785306042, 
0.00341326958812737, 0.00486047258919852, 0.007542225952082, 
0.00869139338671661, 0.00873989639166249, 0.00959555688266032, 
0.00981782767805792, 0.00993716125661212, 0.0113267469127579, 
0.0128318926960938, 0.0140529488804914, 0.0180028284113852, 0.0273477266839752, 
0.0294878478497343, 0.034989937779684, 0.0357257314479433, 0.0415793911120156, 
0.0457740965635417, 0.0462249434190444), gene_symbol = c("Ifit1", 
"Cxcl10", "Rsad2", "Ifit3", "Bst2", "C3", "Irf7", "Ctss", "Zc3hav1", 
"Egr1", "Isg15", "Gbp2", "Ifih1", "Tifa", "C1qb", "Mertk", "Ifitm3", 
"Egfr", "Irf1", "Aim2", "Socs3", "Mx1", "Vegfa", "Irf9", "Cd53", 
"Xdh", "Hmgb2", "Cxcl14", "Hmox1", "Trim67", "Ppargc1a", "Nlrc5", 
"Tnfrsf1a", "Zfpm2", "Eif2ak2", "Birc5", "Eps8", "Stat1", "Ifi27", 
"Trim5"), entrezgene = c(15957L, 15945L, 58185L, 15959L, 69550L, 
102642251L, 54123L, 13040L, 78781L, 13653L, 100038882L, 14469L, 
71586L, 211550L, 12260L, 17289L, 66141L, 13649L, 16362L, 383619L, 
12702L, NA, 22339L, 16391L, 12508L, 22436L, 97165L, 57266L, 15368L, 
330863L, 19017L, 434341L, 21937L, 22762L, 19106L, 11799L, 13860L, 
20846L, 52668L, 667823L), description = structure(c(19L, 6L, 
30L, 20L, 3L, 10L, 24L, 4L, 39L, 11L, 26L, 15L, 22L, 33L, 9L, 
8L, 21L, 13L, 23L, 1L, 32L, 27L, 37L, 25L, 5L, 38L, 17L, 7L, 
16L, 35L, 29L, 28L, 36L, 40L, 14L, 2L, 12L, 31L, 18L, 34L), .Label = c("absent in melanoma 2 [Source:MGI Symbol;Acc:MGI:2686159]", 
"baculoviral IAP repeat-containing 5 [Source:MGI Symbol;Acc:MGI:1203517]", 
"bone marrow stromal cell antigen 2 [Source:MGI Symbol;Acc:MGI:1916800]", 
"cathepsin S [Source:MGI Symbol;Acc:MGI:107341]", "CD53 antigen [Source:MGI Symbol;Acc:MGI:88341]", 
"chemokine (C-X-C motif) ligand 10 [Source:MGI Symbol;Acc:MGI:1352450]", 
"chemokine (C-X-C motif) ligand 14 [Source:MGI Symbol;Acc:MGI:1888514]", 
"c-mer proto-oncogene tyrosine kinase [Source:MGI Symbol;Acc:MGI:96965]", 
"complement component 1, q subcomponent, beta polypeptide [Source:MGI Symbol;Acc:MGI:88224]", 
"complement component 3 [Source:MGI Symbol;Acc:MGI:88227]", "early growth response 1 [Source:MGI Symbol;Acc:MGI:95295]", 
"epidermal growth factor receptor pathway substrate 8 [Source:MGI Symbol;Acc:MGI:104684]", 
"epidermal growth factor receptor [Source:MGI Symbol;Acc:MGI:95294]", 
"eukaryotic translation initiation factor 2-alpha kinase 2 [Source:MGI Symbol;Acc:MGI:1353449]", 
"guanylate binding protein 2 [Source:MGI Symbol;Acc:MGI:102772]", 
"heme oxygenase (decycling) 1 [Source:MGI Symbol;Acc:MGI:96163]", 
"high mobility group box 2 [Source:MGI Symbol;Acc:MGI:96157]", 
"interferon, alpha-inducible protein 27 [Source:MGI Symbol;Acc:MGI:1277180]", 
"interferon-induced protein with tetratricopeptide repeats 1 [Source:MGI Symbol;Acc:MGI:99450]", 
"interferon-induced protein with tetratricopeptide repeats 3 [Source:MGI Symbol;Acc:MGI:1101055]", 
"interferon induced transmembrane protein 3 [Source:MGI Symbol;Acc:MGI:1913391]", 
"interferon induced with helicase C domain 1 [Source:MGI Symbol;Acc:MGI:1918836]", 
"interferon regulatory factor 1 [Source:MGI Symbol;Acc:MGI:96590]", 
"interferon regulatory factor 7 [Source:MGI Symbol;Acc:MGI:1859212]", 
"interferon regulatory factor 9 [Source:MGI Symbol;Acc:MGI:107587]", 
"ISG15 ubiquitin-like modifier [Source:MGI Symbol;Acc:MGI:1855694]", 
"myxovirus (influenza virus) resistance 1 [Source:MGI Symbol;Acc:MGI:97243]", 
"NLR family, CARD domain containing 5 [Source:MGI Symbol;Acc:MGI:3612191]", 
"peroxisome proliferative activated receptor, gamma, coactivator 1 alpha [Source:MGI Symbol;Acc:MGI:1342774]", 
"radical S-adenosyl methionine domain containing 2 [Source:MGI Symbol;Acc:MGI:1929628]", 
"signal transducer and activator of transcription 1 [Source:MGI Symbol;Acc:MGI:103063]", 
"suppressor of cytokine signaling 3 [Source:MGI Symbol;Acc:MGI:1201791]", 
"TRAF-interacting protein with forkhead-associated domain [Source:MGI Symbol;Acc:MGI:2182965]", 
"tripartite motif-containing 5 [Source:MGI Symbol;Acc:MGI:3646853]", 
"tripartite motif-containing 67 [Source:MGI Symbol;Acc:MGI:3045323]", 
"tumor necrosis factor receptor superfamily, member 1a [Source:MGI Symbol;Acc:MGI:1314884]", 
"vascular endothelial growth factor A [Source:MGI Symbol;Acc:MGI:103178]", 
"xanthine dehydrogenase [Source:MGI Symbol;Acc:MGI:98973]", "zinc finger CCCH type, antiviral 1 [Source:MGI Symbol;Acc:MGI:1926031]", 
"zinc finger protein, multitype 2 [Source:MGI Symbol;Acc:MGI:1334444]"
), class = "factor"), gene_biotype = structure(c(1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L), .Label = "protein_coding", class = "factor")), row.names = c(NA, 
-40L), .Names = c("ensembl_gene_id", "baseMean", "log2FoldChange", 
"lfcSE", "stat", "pvalue", "padj", "gene_symbol", "entrezgene", 
"description", "gene_biotype"), class = "data.frame")
```

```{r}
innateDB_bulk_up_genes <- innateDB_genes_NSCs_bulk_up_table %>% dplyr::arrange( desc( log2FoldChange )) %>% ungroup() %>% dplyr::select(ensembl_gene_id , gene_symbol )

innateDB_bulk_up_genes <- innateDB_bulk_up_genes[1:7,]
```

We split the df into a list , rows become fields

```{r}
genes_up_InnateDB_sms2_bulk_list <- split(innateDB_bulk_up_genes , f = seq(nrow(innateDB_bulk_up_genes)))
```

Next up we calculate the totalReads and reads for only the specified gene, add the information for cells which age and celltype they belong to.

```{r message=FALSE, warning=FALSE}
genes_up_InnateDB_data_list <- lapply(genes_up_InnateDB_sms2_bulk_list, function(x){
  raw_count_data <- data.frame( totalReads = colSums(data_10X_NSCs[,as.character(cells_NSCs_10X)]) , geneReads = as.numeric(data_10X_NSCs[ as.character(x$ensembl_gene_id) ,as.character(cells_NSCs_10X)]) , gene = as.character(x$gene_symbol) )  %>% rownames_to_column("name") %>% left_join(anno_10X_NSCs , by = "name" )

  table_cells_expr <- addmargins( table( raw_count_data$age  , raw_count_data$geneReads > 0 ) )

  # fig.width=6 , fig.height=2

  gg <- ggplot(data = raw_count_data , mapping = aes(x = totalReads , y = geneReads ) ) + geom_point(aes(color = age)) + scale_color_manual(values = c("old" = "slateblue" , "young" = "yellowgreen") ) + ggtitle(x$gene_symbol)

list( "raw_count_data" = raw_count_data , "numbers_cells_expressing" = table_cells_expr , "plot" = gg  )
})
```

We extract the raw_counts data table from the list we have just created

```{r}
genes_up_InnateDB_data_list_raw_counts <- lapply(genes_up_InnateDB_data_list, function(x){ x$raw_count_data })
```

and bind together all the data frames for the different genes

```{r message=FALSE, warning=FALSE}
genes_up_InnateDB_data_plot_10X <- bind_rows(genes_up_InnateDB_data_list_raw_counts)
```

##### Scatterplot

```{r}
gg_InnateDB_10X <- ggplot(data = genes_up_InnateDB_data_plot_10X , mapping = aes(x = totalReads , y = geneReads ) ) + geom_point(aes(color = age)) + scale_color_manual(values = c("old" = "slateblue" , "young" = "yellowgreen") ) + facet_grid( gene ~ .  )
```

```{r fig.width=10 , fig.height=20}
gg_InnateDB_10X
```

##### Barplot

Show the same data as stacked barplots

```{r}
genes_up_InnateDB_data_plot_10X_bars <- genes_up_InnateDB_data_plot_10X %>% mutate( interval = base::cut( geneReads , breaks = c(-0.1, 0.5,5.5,50.5,Inf) , labels = c("0","1-5","5-50","50-") ) ) %>% group_by(interval,gene,age) %>% summarise( counts = n() ) %>% ungroup() %>% mutate( interval = fct_rev( interval ) , age = fct_recode( .f = age ,  "22 month" = "old",  "2 month" = "young" ) %>% fct_rev() ) %>% group_by(gene,age) %>% mutate( Percent = 100*counts/sum(counts) )
```

```{r fig.width=7, fig.height=5}
ggplot( data = genes_up_InnateDB_data_plot_10X_bars , mapping = aes(x = age , y = Percent , fill = interval) ) + geom_bar(stat = "identity") + facet_grid(facets = . ~ gene ) + viridis::scale_fill_viridis( discrete = TRUE , direction = -1 , drop=FALSE ,  name = "Read count" ) + ggtitle("Read counts: 10X") + theme( axis.text.x = element_text(angle = 90,hjust = 0,vjust = 0.5) , strip.text.x = element_text(size = 8)) 
```

#### 



## SMARTseq2

How many counts do genes have in the Single cell data sets, that are significantly upregulated in the bulk NSCs

```{r}
cells_NSCs_SMARTseq2 <- as.character(anno_SMARTseq2_NSCs$name)
```

### Ifit1 - ENSMUSG00000078920

```{r}
Ifit1_df_SMARTseq2 <- data.frame( totalReads = colSums(data_SMARTseq2_NSCs) , geneReads = as.numeric(data_SMARTseq2_NSCs["ENSMUSG00000078920",cells_NSCs_SMARTseq2]) , gene = "Ifit1" ) %>% rownames_to_column("name") %>% left_join(anno_SMARTseq2_NSCs , by = "name" )
```

```{r}
addmargins( table( Ifit1_df_SMARTseq2$age  , Ifit1_df_SMARTseq2$geneReads > 0 ) )
```

```{r fig.width=6 , fig.height=2}
gg_Ifit1_SMARTseq2 <- ggplot(data = Ifit1_df_SMARTseq2 , mapping = aes(x = totalReads , y = geneReads ) ) + geom_point(aes(color = age)) + scale_color_manual(values = c("old" = "slateblue" , "young" = "yellowgreen") )

gg_Ifit1_SMARTseq2
```

### Rsad2 - ENSMUSG00000020641

```{r}
Rsad2_df_SMARTseq2 <- data.frame( totalReads = colSums(data_SMARTseq2_NSCs) , geneReads = as.numeric(data_SMARTseq2_NSCs["ENSMUSG00000020641",cells_NSCs_SMARTseq2]) , gene = "Rsad2"  ) %>% rownames_to_column("name") %>% left_join(anno_SMARTseq2_NSCs , by = "name" )
```

```{r}
addmargins( table( Rsad2_df_SMARTseq2$age  , Rsad2_df_SMARTseq2$geneReads > 0 ) )
```

```{r fig.width=6 , fig.height=2}
gg_Rsad2_SMARTseq2 <- ggplot(data = Rsad2_df_SMARTseq2 , mapping = aes(x = totalReads , y = geneReads ) ) + geom_point(aes(color = age)) + scale_color_manual(values = c("old" = "slateblue" , "young" = "yellowgreen") )

gg_Rsad2_SMARTseq2
```

### C3 - ENSMUSG00000024164

```{r}
C3_df_SMARTseq2 <- data.frame( totalReads = colSums(data_SMARTseq2_NSCs) , geneReads = as.numeric(data_SMARTseq2_NSCs["ENSMUSG00000024164",cells_NSCs_SMARTseq2]) , gene = "C3"   )  %>% rownames_to_column("name") %>% left_join(anno_SMARTseq2_NSCs , by = "name" )
```

```{r}
addmargins( table( C3_df_SMARTseq2$age  , C3_df_SMARTseq2$geneReads > 0 ) )
```

```{r fig.width=6 , fig.height=2}
gg_C3_SMARTseq2 <- ggplot(data = C3_df_SMARTseq2 , mapping = aes(x = totalReads , y = geneReads ) ) + geom_point(aes(color = age)) + scale_color_manual(values = c("old" = "slateblue" , "young" = "yellowgreen") )

gg_C3_SMARTseq2
```

### Iigp1 - ENSMUSG00000054072

```{r}
Iigp1_df_SMARTseq2 <- data.frame( totalReads = colSums(data_SMARTseq2_NSCs) , geneReads = as.numeric(data_SMARTseq2_NSCs["ENSMUSG00000054072",cells_NSCs_SMARTseq2]) , gene = "Iigp1"   )  %>% rownames_to_column("name") %>% left_join(anno_SMARTseq2_NSCs , by = "name" )
```

```{r}
addmargins( table( Iigp1_df_SMARTseq2$age  , Iigp1_df_SMARTseq2$geneReads > 0 ) )
```

```{r fig.width=6 , fig.height=2}
gg_Iigp1_SMARTseq2 <- ggplot(data = Iigp1_df_SMARTseq2 , mapping = aes(x = totalReads , y = geneReads ) ) + geom_point(aes(color = age)) + scale_color_manual(values = c("old" = "slateblue" , "young" = "yellowgreen") )

gg_Iigp1_SMARTseq2
```


### Ifi44 - ENSMUSG00000028037

```{r}
Ifi44_df_SMARTseq2 <- data.frame( totalReads = colSums(data_SMARTseq2_NSCs) , geneReads = as.numeric(data_SMARTseq2_NSCs["ENSMUSG00000028037",cells_NSCs_SMARTseq2]) , gene = "Ifi44"   )  %>% rownames_to_column("name") %>% left_join(anno_SMARTseq2_NSCs , by = "name" )
```

```{r}
addmargins( table( Ifi44_df_SMARTseq2$age  , Ifi44_df_SMARTseq2$geneReads > 0 ) )
```

```{r fig.width=6 , fig.height=2}
gg_Ifi44_SMARTseq2 <- ggplot(data = Ifi44_df_SMARTseq2 , mapping = aes(x = totalReads , y = geneReads ) ) + geom_point(aes(color = age)) + scale_color_manual(values = c("old" = "slateblue" , "young" = "yellowgreen") )

gg_Ifi44_SMARTseq2
```


### Cxcl10 - ENSMUSG00000034855

```{r}
Cxcl10_df_SMARTseq2 <- data.frame( totalReads = colSums(data_SMARTseq2_NSCs) , geneReads = as.numeric(data_SMARTseq2_NSCs["ENSMUSG00000034855",cells_NSCs_SMARTseq2]) , gene = "Cxcl10"   )  %>% rownames_to_column("name") %>% left_join(anno_SMARTseq2_NSCs , by = "name" )
```
```{r}
addmargins( table( Cxcl10_df_SMARTseq2$age  , Cxcl10_df_SMARTseq2$geneReads > 0 ) )
```

```{r fig.width=6 , fig.height=2}
gg_Cxcl10_SMARTseq2 <- ggplot(data = Cxcl10_df_SMARTseq2 , mapping = aes(x = totalReads , y = geneReads ) ) + geom_point(aes(color = age)) + scale_color_manual(values = c("old" = "slateblue" , "young" = "yellowgreen") )

gg_Cxcl10_SMARTseq2
```

### Il33 - ENSMUSG00000024810

```{r}
Il33_df_SMARTseq2 <- data.frame( totalReads = colSums(data_SMARTseq2_NSCs) , geneReads = as.numeric(data_SMARTseq2_NSCs["ENSMUSG00000024810",cells_NSCs_SMARTseq2]) , gene = "Il33"   )  %>% rownames_to_column("name") %>% left_join(anno_SMARTseq2_NSCs , by = "name" )
```

```{r}
addmargins( table( Il33_df_SMARTseq2$age  , Il33_df_SMARTseq2$geneReads > 0 ) )
```

```{r fig.width=6 , fig.height=2}
gg_Il33_SMARTseq2 <- ggplot(data = Il33_df_SMARTseq2 , mapping = aes(x = totalReads , y = geneReads ) ) + geom_point(aes(color = age)) + scale_color_manual(values = c("old" = "slateblue" , "young" = "yellowgreen") )

gg_Il33_SMARTseq2
```

### Ddx60 - ENSMUSG00000037921

```{r}
Ddx60_df_SMARTseq2 <- data.frame( totalReads = colSums(data_SMARTseq2_NSCs) , geneReads = as.numeric(data_SMARTseq2_NSCs["ENSMUSG00000037921",cells_NSCs_SMARTseq2]) , gene = "Ddx60"   ) %>% rownames_to_column("name") %>% left_join(anno_SMARTseq2_NSCs , by = "name" )
```

```{r}
addmargins( table( Ddx60_df_SMARTseq2$age  , Ddx60_df_SMARTseq2$geneReads > 0 ) )
```

```{r fig.width=6 , fig.height=2}
gg_Ddx60_SMARTseq2 <- ggplot(data = Ddx60_df_SMARTseq2 , mapping = aes(x = totalReads , y = geneReads ) ) + geom_point(aes(color = age)) + scale_color_manual(values = c("old" = "slateblue" , "young" = "yellowgreen") )

gg_Ddx60_SMARTseq2
```

### Ifi47 - ENSMUSG00000078920

```{r}
Ifi47_df_SMARTseq2 <- data.frame( totalReads = colSums(data_SMARTseq2_NSCs) , geneReads = as.numeric(data_SMARTseq2_NSCs["ENSMUSG00000078920",cells_NSCs_SMARTseq2]) , gene = "Ifi47"   ) %>% rownames_to_column("name") %>% left_join(anno_SMARTseq2_NSCs , by = "name" )
```

```{r}
addmargins( table( Ifi47_df_SMARTseq2$age  , Ifi47_df_SMARTseq2$geneReads > 0 ) )
```

```{r fig.width=6 , fig.height=2}
gg_Ifi47_SMARTseq2 <- ggplot(data = Ifi47_df_SMARTseq2 , mapping = aes(x = totalReads , y = geneReads ) ) + geom_point(aes(color = age)) + scale_color_manual(values = c("old" = "slateblue" , "young" = "yellowgreen") )

gg_Ifi47_SMARTseq2
```

### Gm4951 - ENSMUSG00000073555

```{r}
Gm4951_df_SMARTseq2 <- data.frame( totalReads = colSums(data_SMARTseq2_NSCs) , geneReads = as.numeric(data_SMARTseq2_NSCs["ENSMUSG00000073555",cells_NSCs_SMARTseq2]) , gene = "Gm4951"   ) %>% rownames_to_column("name") %>% left_join(anno_SMARTseq2_NSCs , by = "name" )
```

```{r}
addmargins( table( Gm4951_df_SMARTseq2$age  , Gm4951_df_SMARTseq2$geneReads > 0 ) )
```

```{r fig.width=6 , fig.height=2}
gg_Gm4951_SMARTseq2 <- ggplot(data = Gm4951_df_SMARTseq2 , mapping = aes(x = totalReads , y = geneReads ) ) + geom_point(aes(color = age)) + scale_color_manual(values = c("old" = "slateblue" , "young" = "yellowgreen") )

gg_Gm4951_SMARTseq2
```

### Slc1a3 - ENSMUSG00000005360

```{r}
Slc1a3_df_SMARTseq2 <- data.frame( totalReads = colSums(data_SMARTseq2_NSCs) , geneReads = as.numeric(data_SMARTseq2_NSCs["ENSMUSG00000005360",cells_NSCs_SMARTseq2]) , gene = "Slc1a3"   ) %>% rownames_to_column("name") %>% left_join(anno_SMARTseq2_NSCs , by = "name" )
```

```{r}
addmargins( table( Slc1a3_df_SMARTseq2$age  , Slc1a3_df_SMARTseq2$geneReads > 0 ) )
```

```{r fig.width=6 , fig.height=2}
gg_Slc1a3_SMARTseq2 <- ggplot(data = Slc1a3_df_SMARTseq2 , mapping = aes(x = totalReads , y = geneReads ) ) + geom_point(aes(color = age)) + scale_color_manual(values = c("old" = "slateblue" , "young" = "yellowgreen") )

gg_Slc1a3_SMARTseq2
```

### Sox2 - ENSMUSG00000074637

```{r}
Sox2_df_SMARTseq2 <- data.frame( totalReads = colSums(data_SMARTseq2_NSCs) , geneReads = as.numeric(data_SMARTseq2_NSCs["ENSMUSG00000074637",cells_NSCs_SMARTseq2]) , gene = "Sox2"   ) %>% rownames_to_column("name") %>% left_join(anno_SMARTseq2_NSCs , by = "name" )
```

```{r}
addmargins( table( Sox2_df_SMARTseq2$age  , Sox2_df_SMARTseq2$geneReads > 0 ) )
```


```{r fig.width=6 , fig.height=2}
gg_Sox2_SMARTseq2 <- ggplot(data = Sox2_df_SMARTseq2 , mapping = aes(x = totalReads , y = geneReads ) ) + geom_point(aes(color = age)) + scale_color_manual(values = c("old" = "slateblue" , "young" = "yellowgreen") )

gg_Sox2_SMARTseq2
```

### Combine all genes

```{r}
interferon_genes_bulk_df_SMARTseq2 <- bind_rows( Ifit1_df_SMARTseq2 , C3_df_SMARTseq2 , Rsad2_df_SMARTseq2 , Iigp1_df_SMARTseq2 , Ifi47_df_SMARTseq2 , Il33_df_SMARTseq2 , Cxcl10_df_SMARTseq2 , Sox2_df_SMARTseq2 , Slc1a3_df_SMARTseq2 )
```

#### Scatterplot

```{r}
gg_interferon_genes_SMARTseq2 <- ggplot(data = interferon_genes_bulk_df_SMARTseq2 , mapping = aes(x = totalReads , y = geneReads ) ) + geom_point(aes(color = age)) + scale_color_manual(values = c("old" = "slateblue" , "young" = "yellowgreen") ) + facet_grid( gene ~ . ) + scale_y_log10() + ylab("log10( geneReads )")
```


```{r fig.width=10 , fig.height=12}
gg_interferon_genes_SMARTseq2
```

#### Barplot

Show the same data as stacked barplots

```{r}
interferon_genes_bulk_df_SMARTseq2_bars <- interferon_genes_bulk_df_SMARTseq2 %>% mutate( interval = base::cut( geneReads , breaks = c(-0.1, 0.5,5.5,50.5,Inf) , labels = c("0","1-5","5-50","50-") ) ) %>% group_by(interval,gene,age) %>% summarise( counts = n() ) %>% ungroup() %>% mutate( interval = fct_rev( interval ) , age = fct_recode( .f = age ,  "22 month" = "old",  "2 month" = "young" ) %>% fct_rev() ) %>% group_by(gene,age) %>% mutate( Percent = 100*counts/sum(counts) )
```

```{r fig.width=18, fig.height=5}
ggplot( data = interferon_genes_bulk_df_SMARTseq2_bars , mapping = aes(x = age , y = Percent , fill = interval) ) + geom_bar(stat = "identity") + facet_grid(facets = . ~ gene ) + viridis::scale_fill_viridis( discrete = TRUE , direction = -1 ) + ggtitle("Read counts: SMARTseq2")
```


### Bulk NSC genes in InnateDB

Which genes are the ones with high fold changes in the bulk and are annotated in InnateDB

```{r}
innateDB_bulk_up_genes <- innateDB_genes_NSCs_bulk_up_table %>% dplyr::arrange( desc( log2FoldChange )) %>% ungroup() %>% dplyr::select(ensembl_gene_id , gene_symbol )

innateDB_bulk_up_genes <- innateDB_bulk_up_genes[1:7,]
```

We split the df into a list , rows become fields

```{r}
genes_up_InnateDB_sms2_bulk_list <- split(innateDB_bulk_up_genes , f = seq(nrow(innateDB_bulk_up_genes)))
```

Next up we calculate the totalReads and reads for only the specified gene, add the information for cells which age and celltype they belong to.

```{r message=FALSE, warning=FALSE}
genes_up_InnateDB_data_list_SMARTseq2 <- lapply(genes_up_InnateDB_sms2_bulk_list, function(x){
  raw_count_data <- data.frame( totalReads = colSums(data_SMARTseq2_NSCs[,as.character(cells_NSCs_SMARTseq2)]) , geneReads = as.numeric(data_SMARTseq2_NSCs[ as.character(x$ensembl_gene_id) ,as.character(cells_NSCs_SMARTseq2) ]) , gene = as.character( x$gene_symbol ) )  %>% rownames_to_column("name") %>% left_join(anno_SMARTseq2_NSCs , by = "name" )

  table_cells_expr <- addmargins( table( raw_count_data$age  , raw_count_data$geneReads > 0 ) )

  # fig.width=6 , fig.height=2

  gg <- ggplot(data = raw_count_data , mapping = aes(x = totalReads , y = geneReads ) ) + geom_point(aes(color = age)) + scale_color_manual(values = c("old" = "slateblue" , "young" = "yellowgreen") ) + ggtitle(x$gene_symbol)

list( "raw_count_data" = raw_count_data , "numbers_cells_expressing" = table_cells_expr , "plot" = gg  )
})
```

We extract the raw_counts data table from the list we have just created

```{r}
genes_up_InnateDB_data_list_SMARTseq2_raw_counts <- lapply(genes_up_InnateDB_data_list_SMARTseq2, function(x){ x$raw_count_data })
```

and bind together all the data frames for the different genes

```{r message=FALSE, warning=FALSE}
genes_up_InnateDB_data_plot_SMARTseq2 <- bind_rows(genes_up_InnateDB_data_list_SMARTseq2_raw_counts)
```

##### Scatterplot

```{r}
gg_InnateDB_SMARTseq2 <- ggplot(data = genes_up_InnateDB_data_plot_SMARTseq2 , mapping = aes(x = totalReads , y = geneReads ) ) + geom_point(aes(color = age)) + scale_color_manual(values = c("old" = "slateblue" , "young" = "yellowgreen") ) + facet_grid( gene ~ .  )
```

```{r fig.width=10 , fig.height=20}
gg_InnateDB_SMARTseq2
```

##### Barplot

Show the same data as stacked barplots

```{r}
genes_up_InnateDB_data_plot_SMARTseq2_bars <- genes_up_InnateDB_data_plot_SMARTseq2 %>% mutate( interval = base::cut( geneReads , breaks = c(-0.1, 0.5,5.5,50.5,Inf) , labels = c("0","1-5","5-50","50-") ) ) %>% group_by(interval,gene,age) %>% summarise( counts = n() ) %>% ungroup() %>% mutate( interval = fct_rev( interval ) , age = fct_recode( .f = age ,  "22 month" = "old",  "2 month" = "young" ) %>% fct_rev() ) %>% group_by(gene,age) %>% mutate( Percent = 100*counts/sum(counts) )
```

```{r fig.width=7, fig.height=5}
ggplot( data = genes_up_InnateDB_data_plot_SMARTseq2_bars , mapping = aes(x = age , y = Percent , fill = interval) ) + geom_bar(stat = "identity") + facet_grid(facets = . ~ gene ) + viridis::scale_fill_viridis( discrete = TRUE , drop=FALSE , direction = -1 , name = "Read count" ) + ggtitle("Read counts: SMARTseq2") + theme( axis.text.x = element_text(angle = 90,hjust = 0,vjust = 0.5))
```


## Combined Barplots for 10X and SMARTseq2

```{r}
genes_up_InnateDB_data_barplot <- bind_rows(
  dplyr::mutate( genes_up_InnateDB_data_plot_SMARTseq2_bars , seqmethod = "Smart-Seq2" ) , 
  dplyr::mutate( genes_up_InnateDB_data_plot_10X_bars  , seqmethod = "10X" ) 
)
```


```{r fig.width=4.1 , fig.height=6}
idx = 1

gg1 <- ggplot( data = genes_up_InnateDB_data_barplot %>% dplyr::filter( gene == innateDB_bulk_up_genes$gene_symbol[[idx]] ) , mapping = aes(x = age , y = Percent , fill = interval) ) + geom_bar(stat = "identity") + facet_grid(facets = . ~ seqmethod ) + viridis::scale_fill_viridis( discrete = TRUE , drop=FALSE , direction = -1 , name = "Read count" ) + ggtitle( paste("Read counts:", innateDB_bulk_up_genes$gene_symbol[[idx]] ) ) + theme( axis.text.x = element_text(angle = 90,hjust = 0,vjust = 0.5))

gg1
```

```{r  fig.width=4.1 , fig.height=6}
idx = 2

 gg2 <- ggplot( data = genes_up_InnateDB_data_barplot %>% dplyr::filter( gene == innateDB_bulk_up_genes$gene_symbol[idx] ) , mapping = aes(x = age , y = Percent , fill = interval) ) + geom_bar(stat = "identity") + facet_grid(facets = . ~ seqmethod ) + viridis::scale_fill_viridis( discrete = TRUE , drop=FALSE , direction = -1 , name = "Read count" ) + ggtitle( paste("Read counts:", innateDB_bulk_up_genes$gene_symbol[[idx]] ) ) + theme( axis.text.x = element_text(angle = 90,hjust = 0,vjust = 0.5))
 
 gg2
```

```{r  fig.width=4.1 , fig.height=6}
idx = 3

gg3 <- ggplot( data = genes_up_InnateDB_data_barplot %>% dplyr::filter( gene == innateDB_bulk_up_genes$gene_symbol[idx] ) , mapping = aes(x = age , y = Percent , fill = interval) ) + geom_bar(stat = "identity") + facet_grid(facets = . ~ seqmethod ) + viridis::scale_fill_viridis( discrete = TRUE , drop=FALSE , direction = -1 , name = "Read count" ) + ggtitle( paste("Read counts:", innateDB_bulk_up_genes$gene_symbol[[idx]] ) ) + theme( axis.text.x = element_text(angle = 90,hjust = 0,vjust = 0.5))

gg3
```

```{r  fig.width=4.1 , fig.height=6}
idx = 4

gg4 <- ggplot( data = genes_up_InnateDB_data_barplot %>% dplyr::filter( gene == innateDB_bulk_up_genes$gene_symbol[idx] ) , mapping = aes(x = age , y = Percent , fill = interval) ) + geom_bar(stat = "identity") + facet_grid(facets = . ~ seqmethod ) + viridis::scale_fill_viridis( discrete = TRUE , drop=FALSE , direction = -1 , name = "Read count" ) + ggtitle( paste("Read counts:", innateDB_bulk_up_genes$gene_symbol[[idx]] ) ) + theme( axis.text.x = element_text(angle = 90,hjust = 0,vjust = 0.5))

gg4
```

```{r  fig.width=4.1 , fig.height=6}
idx = 5

gg5 <- ggplot( data = genes_up_InnateDB_data_barplot %>% dplyr::filter( gene == innateDB_bulk_up_genes$gene_symbol[idx] ) , mapping = aes(x = age , y = Percent , fill = interval) ) + geom_bar(stat = "identity") + facet_grid(facets = . ~ seqmethod ) + viridis::scale_fill_viridis( discrete = TRUE , drop=FALSE , direction = -1 , name = "Read count" ) + ggtitle( paste("Read counts:", innateDB_bulk_up_genes$gene_symbol[[idx]] ) ) + theme( axis.text.x = element_text(angle = 90,hjust = 0,vjust = 0.5))

gg5
```

```{r  fig.width=4.1 , fig.height=6}
idx = 6

gg6 <- ggplot( data = genes_up_InnateDB_data_barplot %>% dplyr::filter( gene == innateDB_bulk_up_genes$gene_symbol[idx] ) , mapping = aes(x = age , y = Percent , fill = interval) ) + geom_bar(stat = "identity") + facet_grid(facets = . ~ seqmethod ) + viridis::scale_fill_viridis( discrete = TRUE , drop=FALSE , direction = -1 , name = "Read count" ) + ggtitle( paste("Read counts:", innateDB_bulk_up_genes$gene_symbol[[idx]] ) ) + theme( axis.text.x = element_text(angle = 90,hjust = 0,vjust = 0.5))

gg6
```

```{r  fig.width=4.1 , fig.height=6}
idx = 7

gg7 <- ggplot( data = genes_up_InnateDB_data_barplot %>% dplyr::filter( gene == innateDB_bulk_up_genes$gene_symbol[idx] ) , mapping = aes(x = age , y = Percent , fill = interval) ) + geom_bar(stat = "identity") + facet_grid(facets = . ~ seqmethod ) + viridis::scale_fill_viridis( discrete = TRUE , drop=FALSE , direction = -1 , name = "Read count" ) + ggtitle( paste("Read counts:", innateDB_bulk_up_genes$gene_symbol[[idx]] ) ) + theme( axis.text.x = element_text(angle = 90,hjust = 0,vjust = 0.5))

gg7
```

```{r fig.width=30 , fig.height=5}
gridExtra::grid.arrange(gg1, gg2, gg3 , gg4, gg5, gg6 , gg7 , nrow = 1)
```


# SessionInfo

```{r}
sessionInfo()
```
